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Correction to: Structural measures of personal networks predict migrants’ cultural backgrounds: an explanation from Grid/Group theory | 10.1093/pnasnexus/pgad469 | https://doi.org/10.1093/pnasnexus/pgad469 | npj Clean Energy | 2,023 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |||
New estimates of the storage permanence and ocean co-benefits of enhanced rock weathering | 10.1093/pnasnexus/pgad059 | https://doi.org/10.1093/pnasnexus/pgad059 | npj Clean Energy | 2,023 | Kanzaki, Y.; Planavsky, N.; Reinhard, C. | Abstract
Avoiding many of the most severe consequences of anthropogenic climate change in the coming century will very likely require the development of “negative emissions technologies”—practices that lead to net carbon dioxide removal (CDR) from Earth's atmosphere. However, feedbacks within the carbon cycle place intrinsic limits on the long-term impact of CDR on atmospheric CO2 that are likely to vary across CDR technologies in ways that are poorly constrained. Here, we use an | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
Transformer fault diagnosis method based on TLR-ADASYN balanced dataset | 10.1038/s41598-023-49901-9 | https://doi.org/10.1038/s41598-023-49901-9 | Scientific Reports | 2,023 | Guan, S.; Yang, H.; Wu, T. | AbstractAs the cornerstone of transmission and distribution equipment, power transformer plays a very important role in ensuring the safe operation of power system. At present, the technology of dissolved gas analysis (DGA) has been widely used in fault diagnosis of oil-immersed transformer. However, in the actual scene, the limited number of transformer fault samples and the uneven distribution of different fault types often lead to low overall fault detection accuracy or a few types of fault m | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
The role of double-skin facade configurations in optimizing building energy performance in Erbil city | 10.1038/s41598-023-35555-0 | https://doi.org/10.1038/s41598-023-35555-0 | Scientific Reports | 2,023 | Naddaf, M.; Baper, S. | AbstractCarefully designing a building facade is the most crucial way to save energy, and a double-skin facade is an effective strategy for achieving energy efficiency. The improvement that can be made depends on how the double-skin facade is set up and what the weather conditions are like. This study was designed to investigate the best-case scenario with an appropriate double-skin facade configuration for optimizing building energy performance. A methodology for optimizing the building's initi | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
A big data association rule mining based approach for energy building behaviour analysis in an IoT environment | 10.1038/s41598-023-47056-1 | https://doi.org/10.1038/s41598-023-47056-1 | Scientific Reports | 2,023 | Dolores, M.; Fernandez-Basso, C.; Gómez-Romero, J.; Martin-Bautista, M. | AbstractThe enormous amount of data generated by sensors and other data sources in modern grid management systems requires new infrastructures, such as IoT (Internet of Things) and Big Data architectures. This, in combination with Data Mining techniques, allows the management and processing of all these heterogeneous massive data in order to discover new insights that can help to reduce the energy consumption of the building. In this paper, we describe a developed methodology for an Internet of | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Analysis of renewable energy consumption and economy considering the joint optimal allocation of “renewable energy + energy storage + synchronous condenser” | 10.1038/s41598-023-47401-4 | https://doi.org/10.1038/s41598-023-47401-4 | Scientific Reports | 2,023 | Wang, Z.; Li, Q.; Kong, S.; Li, W.; Luo, J. | Abstract
As renewable energy becomes increasingly dominant in the energy mix, the power system is evolving towards high proportions of renewable energy installations and power electronics-based equipment. This transition introduces significant challenges to the grid’s safe and stable operation. On the one hand, renewable energy generation equipment inherently provides weak voltage support, necessitating improvements in the voltage support capacity at renewable energy grid point | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Optimizing upside variability and antifragility in renewable energy system design | 10.1038/s41598-023-36379-8 | https://doi.org/10.1038/s41598-023-36379-8 | Scientific Reports | 2,023 | Coppitters, D.; Contino, F. | AbstractDespite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Interrelationships between urban travel demand and electricity consumption: a deep learning approach | 10.1038/s41598-023-33133-y | https://doi.org/10.1038/s41598-023-33133-y | Scientific Reports | 2,023 | Movahedi, A.; Parsa, A.; Rozhkov, A.; Lee, D.; Mohammadian, A. | AbstractThe analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long S | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
Electricity consumption in Finland influenced by climate effects of energetic particle precipitation | 10.1038/s41598-023-47605-8 | https://doi.org/10.1038/s41598-023-47605-8 | Scientific Reports | 2,023 | Juntunen, V.; Asikainen, T. | AbstractIt is known that electricity consumption in many cold Northern countries depends greatly on prevailing outdoor temperatures especially during the winter season. On the other hand, recent research has demonstrated that solar wind driven energetic particle precipitation from space into the polar atmosphere can influence the stratospheric polar vortex and tropospheric weather patterns during winter. These changes are significant, e.g., in Northern Europe, especially in Finland. In this stud | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
Author Correction: Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data | 10.1038/s41598-023-28997-z | https://doi.org/10.1038/s41598-023-28997-z | Scientific Reports | 2,023 | Chua, Z.; Evans, A.; Kuleshov, Y.; Watkins, A.; Choy, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Optimized scheduling study of user side energy storage in cloud energy storage model | 10.1038/s41598-023-45673-4 | https://doi.org/10.1038/s41598-023-45673-4 | Scientific Reports | 2,023 | Wang, H.; Yao, H.; Zhou, J.; Guo, Q. | AbstractWith the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Simulation of melting paraffin with graphene nanoparticles within a solar thermal energy storage system | 10.1038/s41598-023-35361-8 | https://doi.org/10.1038/s41598-023-35361-8 | Scientific Reports | 2,023 | Jafaryar, M.; Sheikholeslami, M. | AbstractIn this paper, applying new structure and loading Graphene nanoparticles have been considered as promising techniques for enhancing thermal storage systems. The layers within the paraffin zone were made from aluminum and the melting temperature of paraffin is 319.55 K. The paraffin zone located in the middle section of the triplex tube and uniform hot temperatures (335 K) for both walls of annulus have been applied. Three geometries for the container were applied with changing the angle | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
Forecasting the carbon footprint of civil buildings under different floor area growth trends and varying energy supply methods | 10.1038/s41598-023-49270-3 | https://doi.org/10.1038/s41598-023-49270-3 | Scientific Reports | 2,023 | Teng, J.; Yin, H. | AbstractThe energy consumption and carbon footprint of buildings are significantly impacted by variations in building area and the number of households. Therefore, it is crucial to forecast the growth trend of building area and number of households. A validated time series model is used to predict the new building area in Jilin Province from 2023 to 2030. The new building area in Jilin Province is expected to exhibit two trends of growth in the future: rapid growth (S1) and slow growth (S2). By | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | |
Blending controlled-release urea and urea under ridge-furrow with plastic film mulching improves yield while mitigating carbon footprint in rainfed potato | 10.1038/s41598-022-25845-4 | https://doi.org/10.1038/s41598-022-25845-4 | Scientific Reports | 2,023 | Sun, M.; Ma, B.; Lu, P.; Bai, J.; Mi, J. | AbstractRidge-furrow with plastic film mulching and various urea types have been applied in rainfed agriculture, but their interactive effects on potato (Solanum tuberosum L.) yield and especially environments remain poorly understood. A three-year experiment was conducted to explore the responses of tuber yield, methane (CH4) and nitrous oxide (N2O) emissions, net global warming potential (NGWP), carbon footprint (CF), and net ecosystem economic budget (NEEB) of rainfed potato to two mulching p | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response | 10.1038/s41598-023-46804-7 | https://doi.org/10.1038/s41598-023-46804-7 | Scientific Reports | 2,023 | Zang, Y.; Wang, S.; Ge, W.; Li, Y.; Cui, J. | AbstractIndustrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and de | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Impact of implementing emergency demand response program and tie-line on cyber-physical distribution network resiliency | 10.1038/s41598-023-30746-1 | https://doi.org/10.1038/s41598-023-30746-1 | Scientific Reports | 2,023 | Osman, S.; Sedhom, B.; Kaddah, S. | AbstractRecently, due to the complex nature of cyber-physical distribution networks (DNs) and the severity of power outages caused by natural disasters, microgrid (MG) formation, distributed renewable energy resources (DRERs), and demand response programs (DRP) have been employed to enhance the resiliency of these networks. This paper proposes a novel multi-objective MGs formation method-based darts game theory optimization algorithm. The microgrid formation is obtained by controlling the sectio | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
The value of fusion energy to a decarbonized United States electric grid | 10.1016/j.joule.2023.02.006 | https://doi.org/10.1016/j.joule.2023.02.006 | Joule | 2,023 | Schwartz, J.; Ricks, W.; Kolemen, E.; Jenkins, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Coordinating distributed energy resources for reliability can significantly reduce future distribution grid upgrades and peak load | 10.1016/j.joule.2023.06.015 | https://doi.org/10.1016/j.joule.2023.06.015 | Joule | 2,023 | Navidi, T.; El Gamal, A.; Rajagopal, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | ||
Two million European single-family homes could abandon the grid by 2050 | 10.1016/j.joule.2023.09.012 | https://doi.org/10.1016/j.joule.2023.09.012 | Joule | 2,023 | Kleinebrahm, M.; Weinand, J.; Naber, E.; McKenna, R.; Ardone, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Thermally activated batteries and their prospects for grid-scale energy storage | 10.1016/j.joule.2023.02.009 | https://doi.org/10.1016/j.joule.2023.02.009 | Joule | 2,023 | Li, M.; Weller, J.; Reed, D.; Sprenkle, V.; Li, G. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Statistical and machine learning-based durability-testing strategies for energy storage | 10.1016/j.joule.2023.03.008 | https://doi.org/10.1016/j.joule.2023.03.008 | Joule | 2,023 | Harris, S.; Noack, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Reviewing the sociotechnical dynamics of carbon removal | 10.1016/j.joule.2022.11.008 | https://doi.org/10.1016/j.joule.2022.11.008 | Joule | 2,023 | Sovacool, B.; Baum, C.; Low, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Getting methane under control: Paper policies, practical measurements, and the urgent need to verify emissions | 10.1016/j.oneear.2023.04.013 | https://doi.org/10.1016/j.oneear.2023.04.013 | One Earth | 2,023 | Nisbet, E. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Optimization of solar and battery-based hybrid renewable energy system augmented with bioenergy and hydro energy-based dispatchable source | 10.1016/j.isci.2022.105821 | https://doi.org/10.1016/j.isci.2022.105821 | iScience | 2,023 | Memon, S.; Upadhyay, D.; Patel, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Hierarchical approach to evaluating storage requirements for renewable-energy-driven grids | 10.1016/j.isci.2022.105900 | https://doi.org/10.1016/j.isci.2022.105900 | iScience | 2,023 | Mahmud, Z.; Shiraishi, K.; Abido, M.; Sánchez-Pérez, P.; Kurtz, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Region-wise evaluation of price-based demand response programs in Japan’s wholesale electricity market considering microeconomic equilibrium | 10.1016/j.isci.2023.106978 | https://doi.org/10.1016/j.isci.2023.106978 | iScience | 2,023 | Malehmirchegini, L.; Suliman, M.; Farzaneh, H. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Demand Response & IoT | ||
Death spiral of the legacy grid: A game-theoretic analysis of modern grid defection processes | 10.1016/j.isci.2023.106415 | https://doi.org/10.1016/j.isci.2023.106415 | iScience | 2,023 | Navon, A.; Belikov, J.; Ofir, R.; Parag, Y.; Orda, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Intrinsic theta oscillation in the attractor network of grid cells | 10.1016/j.isci.2023.106351 | https://doi.org/10.1016/j.isci.2023.106351 | iScience | 2,023 | Wang, Z.; Wang, T.; Yang, F.; Liu, F.; Wang, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents | 10.1016/j.isci.2023.108102 | https://doi.org/10.1016/j.isci.2023.108102 | iScience | 2,023 | Schøyen, V.; Pettersen, M.; Holzhausen, K.; Fyhn, M.; Malthe-Sørenssen, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Data-driven multi-objective optimization for electric vehicle charging infrastructure | 10.1016/j.isci.2023.107737 | https://doi.org/10.1016/j.isci.2023.107737 | iScience | 2,023 | Farhadi, F.; Wang, S.; Palacin, R.; Blythe, P. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Mind the goal: Trade-offs between flexibility goals for controlled electric vehicle charging strategies | 10.1016/j.isci.2023.105937 | https://doi.org/10.1016/j.isci.2023.105937 | iScience | 2,023 | Gschwendtner, C.; Knoeri, C.; Stephan, A. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
An overview of deterministic and probabilistic forecasting methods of wind energy | 10.1016/j.isci.2022.105804 | https://doi.org/10.1016/j.isci.2022.105804 | iScience | 2,023 | Xie, Y.; Li, C.; Li, M.; Liu, F.; Taukenova, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Spatiotemporal analysis of the future carbon footprint of solar electricity in the United States by a dynamic life cycle assessment | 10.1016/j.isci.2023.106188 | https://doi.org/10.1016/j.isci.2023.106188 | iScience | 2,023 | Lu, J.; Tang, J.; Shan, R.; Li, G.; Rao, P. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Demand Response & IoT | ||
Nonlinear terahertz control of the lead halide perovskite lattice | 10.1126/sciadv.adg3856 | https://doi.org/10.1126/sciadv.adg3856 | Science Advances | 2,023 | Frenzel, M.; Cherasse, M.; Urban, J.; Wang, F.; Xiang, B. |
Lead halide perovskites (LHPs) have emerged as an excellent class of semiconductors for next-generation solar cells and optoelectronic devices. Tailoring physical properties by fine-tuning the lattice structures has been explored in these materials by chemical composition or morphology. Nevertheless, its dynamic counterpart, phonon-driven ultrafast material control, as contemporarily harnessed for oxide perovskites, has not yet been established. Here, we use intense THz electric fie | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Manipulating nitration and stabilization to achieve high energy | 10.1126/sciadv.adk3754 | https://doi.org/10.1126/sciadv.adk3754 | Science Advances | 2,023 | Singh, J.; Staples, R.; Shreeve, J. |
Nitro groups have played a central and decisive role in the development of the most powerful known energetic materials. Highly nitrated compounds are potential oxidizing agents, which could replace the environmentally hazardous used materials such as ammonium perchlorate. The scarcity of azole compounds with a large number of nitro groups is likely due to their inherent thermal instability and the limited number of ring sites available for bond formation. Now, the formation of the f | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant | 10.1126/sciadv.adc9576 | https://doi.org/10.1126/sciadv.adc9576 | Science Advances | 2,023 | Jablonka, K.; Charalambous, C.; Sanchez Fernandez, E.; Wiechers, G.; Monteiro, J. | One of the main environmental impacts of amine-based carbon capture processes is the emission of the solvent into the atmosphere. To understand how these emissions are affected by the intermittent operation of a power plant, we performed stress tests on a plant operating with a mixture of two amines, 2-amino-2-methyl-1-propanol and piperazine (CESAR1). To forecast the emissions and model the impact of interventions, we developed a machine learning model. Our model showed that some interventions | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
Injectable, self-healing hydrogel adhesives with firm tissue adhesion and on-demand biodegradation for sutureless wound closure | 10.1126/sciadv.adh4327 | https://doi.org/10.1126/sciadv.adh4327 | Science Advances | 2,023 | Ren, H.; Zhang, Z.; Cheng, X.; Zou, Z.; Chen, X. |
Tissue adhesives have garnered extensive interest as alternatives and supplements to sutures, whereas major challenges still remain, including weak tissue adhesion, inadequate biocompatibility, and uncontrolled biodegradation. Here, injectable and biocompatible hydrogel adhesives are developed via catalyst-free
o-
phthalaldehyde/amine (hydrazide) cross-linking reaction. The hydrogels demonstrate rapid and firm adhesion to various tissues, and an
o | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Swarming self-adhesive microgels enabled aneurysm on-demand embolization in physiological blood flow | 10.1126/sciadv.adf9278 | https://doi.org/10.1126/sciadv.adf9278 | Science Advances | 2,023 | Jin, D.; Wang, Q.; Chan, K.; Xia, N.; Yang, H. | The recent rise of swarming microrobotics offers great promise in the revolution of minimally invasive embolization procedure for treating aneurysm. However, targeted embolization treatment of aneurysm using microrobots has significant challenges in the delivery capability and filling controllability. Here, we develop an interventional catheterization-integrated swarming microrobotic platform for aneurysm on-demand embolization in physiological blood flow. A pH-responsive self-healing hydrogel d | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Grid-based methods for chemistry simulations on a quantum computer | 10.1126/sciadv.abo7484 | https://doi.org/10.1126/sciadv.abo7484 | Science Advances | 2,023 | Chan, H.; Meister, R.; Jones, T.; Tew, D.; Benjamin, S. | First-quantized, grid-based methods for chemistry modeling are a natural and elegant fit for quantum computers. However, it is infeasible to use today’s quantum prototypes to explore the power of this approach because it requires a substantial number of near-perfect qubits. Here, we use exactly emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms that model 2D and 3D atoms with single and paired particles. A range of tasks is explored, from ground state | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Light-stimulated micromotor swarms in an electric field with accurate spatial, temporal, and mode control | 10.1126/sciadv.adi9932 | https://doi.org/10.1126/sciadv.adi9932 | Science Advances | 2,023 | Liang, Z.; Joh, H.; Lian, B.; Fan, D. | Swarming, a phenomenon widely present in nature, is a hallmark of nonequilibrium living systems that harness external energy into collective locomotion. The creation and study of manmade swarms may provide insights into their biological counterparts and shed light to the rules of life. Here, we propose an innovative mechanism for rationally creating multimodal swarms with unprecedented spatial, temporal, and mode control. The research is realized in a system made of optoelectric semiconductor na | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Toward highly effective loading of DNA in hydrogels for high-density and long-term information storage | 10.1126/sciadv.adg9933 | https://doi.org/10.1126/sciadv.adg9933 | Science Advances | 2,023 | Fei, Z.; Gupta, N.; Li, M.; Xiao, P.; Hu, X. |
Digital information, when converted into a DNA sequence, provides dense, stable, energy-efficient, and sustainable data storage. The most stable method for encapsulating DNA has been in an inorganic matrix of silica, iron oxide, or both, but are limited by low DNA uptake and complex recovery techniques. This study investigated a rationally designed thermally responsive functionally graded (TRFG) hydrogel as a simple and cost-effective method for storing DNA. The TRFG hydrogel shows | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power | 10.1038/s41597-022-01786-5 | https://doi.org/10.1038/s41597-022-01786-5 | Scientific Data | 2,022 | Sterl, S.; Hussain, B.; Miketa, A.; Li, Y.; Merven, B. | AbstractWith solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variabilit | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms | 10.1038/s41597-022-01520-1 | https://doi.org/10.1038/s41597-022-01520-1 | Scientific Data | 2,022 | Chen, X.; Huang, Y.; Nie, C.; Zhang, S.; Wang, G. | AbstractPhotosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | AI & Deep Learning | |
A high spatial resolution dataset for anthropogenic atmospheric mercury emissions in China during 1998–2014 | 10.1038/s41597-022-01725-4 | https://doi.org/10.1038/s41597-022-01725-4 | Scientific Data | 2,022 | Chang, W.; Zhong, Q.; Liang, S.; Qi, J.; Jetashree, . | AbstractChina is the largest atmospheric mercury (Hg) emitter globally, which has been substantially investigated. However, the estimation of national or regional Hg emissions in China is insufficient in supporting emission control, as the location of the sources may have significant impacts on the effects of Hg emissions. In this concern, high-spatial-resolution datasets of China’s Hg emissions are necessary for in-depth and accurate Hg-related studies and policymaking. Existing gridded dataset | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition | 10.1038/s41597-022-01696-6 | https://doi.org/10.1038/s41597-022-01696-6 | Scientific Data | 2,022 | Chen, Y.; Xu, J. | AbstractAccurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
Datasets on South Korean manufacturing factories’ electricity consumption and demand response participation | 10.1038/s41597-022-01357-8 | https://doi.org/10.1038/s41597-022-01357-8 | Scientific Data | 2,022 | Lee, E.; Baek, K.; Kim, J. | AbstractThis study describes the release of electricity consumption data of some manufacturing factories located in South Korea that participate in the demand response (DR) market. The data (in kilowatt) comprise individual factories’ total power usage details that were acquired using advanced metering infrastructures. They further contain details on the manufacture types, DR participation dates, mandatory reduction capacities, and response capacities of the factories. For data acquisition, 10 m | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Demand Response & IoT | |
A residential labeled dataset for smart meter data analytics | 10.1038/s41597-022-01252-2 | https://doi.org/10.1038/s41597-022-01252-2 | Scientific Data | 2,022 | Pereira, L.; Costa, D.; Ribeiro, M. | AbstractSmart meter data is a cornerstone for the realization of next-generation electrical power grids by enabling the creation of novel energy data-based services like providing recommendations on how to save energy or predictive maintenance of electric appliances. Most of these services are developed on top of advanced machine-learning algorithms, which rely heavily on datasets for training, testing, and validation purposes. A limitation of most existing datasets, however, is the scarcity of | CrossRef | FLEXERGY | Smart Home & EMS | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Planning sustainable electricity solutions for refugee settlements in sub-Saharan Africa | 10.1038/s41560-022-01006-9 | https://doi.org/10.1038/s41560-022-01006-9 | Nature Energy | 2,022 | Baldi, D.; Moner-Girona, M.; Fumagalli, E.; Fahl, F. | AbstractAn inadequate understanding of the energy needs of forcibly displaced populations is one of the main obstacles in providing sustainable and reliable energy to refugees and their host communities. Here, we provide a first-order assessment of the main factors determining the decision to deploy fully renewable mini-grids in almost 300 refugee settlements in sub-Saharan Africa. Using an energy assessment survey and publicly available traditional and earth observation data, we estimate a tota | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption | 10.1038/s41560-022-01105-7 | https://doi.org/10.1038/s41560-022-01105-7 | Nature Energy | 2,022 | Powell, S.; Cezar, G.; Min, L.; Azevedo, I.; Rajagopal, R. | AbstractElectric vehicles will contribute to emissions reductions in the United States, but their charging may challenge electricity grid operations. We present a data-driven, realistic model of charging demand that captures the diverse charging behaviours of future adopters in the US Western Interconnection. We study charging control and infrastructure build-out as critical factors shaping charging load and evaluate grid impact under rapid electric vehicle adoption with a detailed economic disp | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Towards a repair research agenda for off-grid solar e-waste in the Global South | 10.1038/s41560-022-01103-9 | https://doi.org/10.1038/s41560-022-01103-9 | Nature Energy | 2,022 | Munro, P.; Samarakoon, S.; Hansen, U.; Kearnes, M.; Bruce, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia | 10.1038/s41467-022-31233-3 | https://doi.org/10.1038/s41467-022-31233-3 | Nature Communications | 2,022 | Riera, J.; Lima, R.; Hoteit, I.; Knio, O. | AbstractThe interdependence between the water and power sectors is a growing concern as the need for desalination increases globally. Therefore, co-optimizing interdependent systems is necessary to understand the impact of one sector on another. We propose a framework to identify the optimal investment mix for a co-optimized water-power system and apply it to Neom, Saudi Arabia. Our results show that investment strategies that consider the co-optimization of both systems result in total cost sav | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation | 10.1038/s41467-022-30164-3 | https://doi.org/10.1038/s41467-022-30164-3 | Nature Communications | 2,022 | Sajadi, A.; Kenyon, R.; Hodge, B. | AbstractThe synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary inte | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Data-driven load profiles and the dynamics of residential electricity consumption | 10.1038/s41467-022-31942-9 | https://doi.org/10.1038/s41467-022-31942-9 | Nature Communications | 2,022 | Anvari, M.; Proedrou, E.; Schäfer, B.; Beck, C.; Kantz, H. | AbstractThe dynamics of power consumption constitutes an essential building block for planning and operating sustainable energy systems. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of the variations in consumption dynamics is still missing. Here, we analyse highly resolved residential electricity consumption data of Austrian, German and UK households and propose a generally applicable data-driven load model. Specifically, | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Electrifying passenger road transport in India requires near-term electricity grid decarbonisation | 10.1038/s41467-022-29620-x | https://doi.org/10.1038/s41467-022-29620-x | Nature Communications | 2,022 | Abdul-Manan, A.; Gordillo Zavaleta, V.; Agarwal, A.; Kalghatgi, G.; Amer, A. | AbstractBattery-electric vehicles (BEV) have emerged as a favoured technology solution to mitigate transport greenhouse gas (GHG) emissions in many non-Annex 1 countries, including India. GHG mitigation potentials of electric 4-wheelers in India depend critically on when and where they are charged: 40% reduction in the north-eastern states and more than 15% increase in the eastern/western regions today, with higher overall GHGs emitted when charged overnight and in the summer. Self-charging gaso | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Disruption of the grid cell network in a mouse model of early Alzheimer’s disease | 10.1038/s41467-022-28551-x | https://doi.org/10.1038/s41467-022-28551-x | Nature Communications | 2,022 | Ying, J.; Keinath, A.; Lavoie, R.; Vigneault, E.; El Mestikawy, S. | Abstract
Early-onset familial Alzheimer’s disease (AD) is marked by an aggressive buildup of amyloid beta (Aβ) proteins, yet the neural circuit operations impacted during the initial stages of Aβ pathogenesis remain elusive. Here, we report a coding impairment of the medial entorhinal cortex (MEC) grid cell network in the J20 transgenic mouse model of familial AD that over-expresses Aβ throughout the hippocampus and entorhinal cortex. Grid cells showed reduced spatial periodici | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Electro-active metaobjective from metalenses-on-demand | 10.1038/s41467-022-34494-0 | https://doi.org/10.1038/s41467-022-34494-0 | Nature Communications | 2,022 | Karst, J.; Lee, Y.; Floess, M.; Ubl, M.; Ludwigs, S. | AbstractSwitchable metasurfaces can actively control the functionality of integrated metadevices with high efficiency and on ultra-small length scales. Such metadevices include active lenses, dynamic diffractive optical elements, or switchable holograms. Especially, for applications in emerging technologies such as AR (augmented reality) and VR (virtual reality) devices, sophisticated metaoptics with unique functionalities are crucially important. In particular, metaoptics which can be switched | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Magnetically assisted drop-on-demand 3D printing of microstructured multimaterial composites | 10.1038/s41467-022-32792-1 | https://doi.org/10.1038/s41467-022-32792-1 | Nature Communications | 2,022 | Liu, W.; Chou, V.; Behera, R.; Le Ferrand, H. | AbstractMicrostructured composites with hierarchically arranged fillers fabricated by three-dimensional (3D) printing show enhanced properties along the fillers’ alignment direction. However, it is still challenging to achieve good control of the filler arrangement and high filler concentration simultaneously, which limits the printed material’s properties. In this study, we develop a magnetically assisted drop-on-demand 3D printing technique (MDOD) to print aligned microplatelet reinforced comp | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities | 10.1093/pnasnexus/pgac077 | https://doi.org/10.1093/pnasnexus/pgac077 | npj Clean Energy | 2,022 | Brown, E.; Johansen, I.; Bortoleto, A.; Pokhrel, Y.; Chaudhari, S. | Abstract
While there have been efforts to supply off-grid energy in the Amazon, these attempts have focused on low upfront costs and deployment rates. These “get-energy-quick” methods have almost solely adopted diesel generators, ignoring the environmental and social risks associated with the known noise and pollution of combustion engines. Alternatively, it is recommended, herein, to supply off-grid needs with renewable, distributed microgrids comprised of photovoltaics (PV) and | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Unexpected no significant soil carbon losses in the Tibetan grasslands due to rodent bioturbation | 10.1093/pnasnexus/pgac314 | https://doi.org/10.1093/pnasnexus/pgac314 | npj Clean Energy | 2,022 | Huang, M.; Gan, D.; Li, Z.; Wang, J.; Niu, S. | AbstractThe Tibetan grasslands store 2.5% of the Earth’s soil organic carbon. Unsound management practices and climate change have resulted in widespread grassland degradation, providing open habitats for rodent activities. Rodent bioturbation loosens topsoil, reduces productivity, changes soil nutrient conditions, and consequently influences the soil organic carbon stocks of the Tibetan grasslands. However, these effects have not been quantified. Here, using meta-analysis and upscaling approach | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
Structural measures of personal networks predict migrants’ cultural backgrounds: an explanation from Grid/Group theory | 10.1093/pnasnexus/pgac195 | https://doi.org/10.1093/pnasnexus/pgac195 | npj Clean Energy | 2,022 | Molina, J.; Ozaita, J.; Tamarit, I.; Sánchez, A.; McCarty, C. | Abstract
Culture and social structure are not separated analytical domains but intertwined phenomena observable in personal networks. Drawing on a personal networks dataset of migrants in the United States and Spain, we show that the country of origin, a proxy for diverse languages and cultural institutions, and religion may be predicted by specific combinations of personal network structural measures (closeness, clustering, betweenness, average degree, etc). We obtain similar res | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Cryocampsis: a biophysical freeze-bending response of shrubs and trees under snow loads | 10.1093/pnasnexus/pgac131 | https://doi.org/10.1093/pnasnexus/pgac131 | npj Clean Energy | 2,022 | Ray, P.; Bret-Harte, M. | Abstract
We report a biophysical mechanism, termed cryocampsis (Greek cryo-, cold, + campsis, bending), that helps northern shrubs bend downward under a snow load. Subfreezing temperatures substantially increase the downward bending of cantilever-loaded branches of these shrubs, while allowing them to recover their summer elevation after thawing and becoming unloaded. This is counterintuitive, because biological materials (including branches that show cryocampsis) generally become | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Energy and thermal modelling of an office building to develop an artificial neural networks model | 10.1038/s41598-022-12924-9 | https://doi.org/10.1038/s41598-022-12924-9 | Scientific Reports | 2,022 | Santos-Herrero, J.; Lopez-Guede, J.; Flores Abascal, I.; Zulueta, E. | AbstractNowadays everyone should be aware of the importance of reducing CO2 emissions which produce the greenhouse effect. In the field of construction, several options are proposed to reach nearly-Zero Energy Building (nZEB) standards. Obviously, before undertaking a modification in any part of a building focused on improving the energy performance, it is generally better to carry out simulations to evaluate its effectiveness. Using Artificial Neural Networks (ANNs) allows a digital twin of the | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data | 10.1038/s41598-022-25255-6 | https://doi.org/10.1038/s41598-022-25255-6 | Scientific Reports | 2,022 | Chua, Z.; Evans, A.; Kuleshov, Y.; Watkins, A.; Choy, S. | AbstractRainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance can be severely limited over regions with low gauge density such as central parts of the continent. At the Australian Bureau of Meteorology, the current operational monthly rainfall component of the Au | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Linking the long-term variability in global wave energy to swell climate and redefining suitable coasts for energy exploitation | 10.1038/s41598-022-18935-w | https://doi.org/10.1038/s41598-022-18935-w | Scientific Reports | 2,022 | Kamranzad, B.; Amarouche, K.; Akpinar, A. | AbstractThe sustainability of wave energy linked to the intra- and inter-annual variability in wave climate is crucial in wave resource assessment. In this study, we quantify the dependency of stability of wave energy flux (power) on long-term variability of wind and wave climate to detect a relationship between them. We used six decades of re-analysis wind and simulated wave climate in the entire globe and using two 30-yearly periods, we showed that not only the previously suggested minimum per | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Authentication of smart grid communications using quantum key distribution | 10.1038/s41598-022-16090-w | https://doi.org/10.1038/s41598-022-16090-w | Scientific Reports | 2,022 | Alshowkan, M.; Evans, P.; Starke, M.; Earl, D.; Peters, N. | AbstractSmart grid solutions enable utilities and customers to better monitor and control energy use via information and communications technology. Information technology is intended to improve the future electric grid’s reliability, efficiency, and sustainability by implementing advanced monitoring and control systems. However, leveraging modern communications systems also makes the grid vulnerable to cyberattacks. Here we report the first use of quantum key distribution (QKD) keys in the authe | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Ecological driving on multiphase trajectories and multiobjective optimization for autonomous electric vehicle platoon | 10.1038/s41598-022-09156-2 | https://doi.org/10.1038/s41598-022-09156-2 | Scientific Reports | 2,022 | Xiaofeng, T. | AbstractAutonomous electric vehicles promise to improve traffic safety, increase fuel efficiency and reduce congestion in future intelligent transportation systems. Ecological driving characteristics are first studied to concentrate on energy consumption, the ability to quickly pass its destination, etc. of autonomous electric vehicle plans (AEVPs) to maximize total energy efficiency benefits. To realize this goal, an optimal control model is developed to provide ecological driving suggestions t | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Collection mode choice of spent electric vehicle batteries: considering collection competition and third-party economies of scale | 10.1038/s41598-022-10433-3 | https://doi.org/10.1038/s41598-022-10433-3 | Scientific Reports | 2,022 | Li, X. | AbstractWith the rapid development of the electric vehicle (EV) industry, the recycling of spent EV batteries has attracted considerable attention. The establishment and optimization of the collection mode is a key link in regulating the recycling of spent EV batteries. This paper investigates an EV battery supply chain including an EV manufacturer, an EV retailer, and a third-party collector and analyzes three dual-channel collection modes. The optimal pricing and collection decisions of the th | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Enhancing wind direction prediction of South Africa wind energy hotspots with Bayesian mixture modeling | 10.1038/s41598-022-14383-8 | https://doi.org/10.1038/s41598-022-14383-8 | Scientific Reports | 2,022 | Rad, N.; Bekker, A.; Arashi, M. | AbstractWind energy production depends not only on wind speed but also on wind direction. Thus, predicting and estimating the wind direction for sites accurately will enhance measuring the wind energy potential. The uncertain nature of wind direction can be presented through probability distributions and Bayesian analysis can improve the modeling of the wind direction using the contribution of the prior knowledge to update the empirical shreds of evidence. This must align with the nature of the | CrossRef | DigiEnergy | Renewable Energy Resource Mapping | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
A weighted energy consumption minimization-based multi-hop uneven clustering routing protocol for cognitive radio sensor networks | 10.1038/s41598-022-18310-9 | https://doi.org/10.1038/s41598-022-18310-9 | Scientific Reports | 2,022 | Wang, J.; Li, C. | AbstractAiming at solving the effective data delivery and energy hole problem in multi-hop cognitive radio sensor networks (CRSNs), a weighted energy consumption minimization-based uneven clustering (ECMUC) routing protocol is proposed in this paper. For the first time, the impact of control overhead on the network performance is taken into consideration, to be specific, the energy consumption of control overhead is integrated with that of data communication to model the network energy consumpti | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Low-carbon economic dispatch considering integrated demand response and multistep carbon trading for multi-energy microgrid | 10.1038/s41598-022-10123-0 | https://doi.org/10.1038/s41598-022-10123-0 | Scientific Reports | 2,022 | Long, Y.; Li, Y.; Wang, Y.; Cao, Y.; Jiang, L. | AbstractWith the rapid development of distributed energy resources and natural gas power generation, multi-energy microgrid (MEMG) is considered as a critical technology to increase the penetration of renewable energy and achieve the target of carbon emission reduction. Therefore, this paper proposes a low-carbon economic dispatch model for MEMG to minimize the daily operation cost by considering integrated demand response (IDR) and multistep carbon trading. Specifically, IDR operation includes | CrossRef | EnergiTrade | Energy & Carbon Trading | Carbon Trading & New Business Models | Optimization & Control | |
Performance optimization of monolithic all-perovskite tandem solar cells under standard and real-world solar spectra | 10.1016/j.joule.2022.06.027 | https://doi.org/10.1016/j.joule.2022.06.027 | Joule | 2,022 | Gao, Y.; Lin, R.; Xiao, K.; Luo, X.; Wen, J. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
The demand-side resource opportunity for deep grid decarbonization | 10.1016/j.joule.2022.04.010 | https://doi.org/10.1016/j.joule.2022.04.010 | Joule | 2,022 | O'Shaughnessy, E.; Shah, M.; Parra, D.; Ardani, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Extreme weather and electricity markets: Key lessons from the February 2021 Texas crisis | 10.1016/j.joule.2021.12.015 | https://doi.org/10.1016/j.joule.2021.12.015 | Joule | 2,022 | Levin, T.; Botterud, A.; Mann, W.; Kwon, J.; Zhou, Z. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | ||
Understanding battery aging in grid energy storage systems | 10.1016/j.joule.2022.09.014 | https://doi.org/10.1016/j.joule.2022.09.014 | Joule | 2,022 | Kumtepeli, V.; Howey, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Policy-driven solar innovation and deployment remains critical for US grid decarbonization | 10.1016/j.joule.2022.07.012 | https://doi.org/10.1016/j.joule.2022.07.012 | Joule | 2,022 | O’Shaughnessy, E.; Ardani, K.; Denholm, P.; Mai, T.; Silverman, T. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Global land-use intensity and anthropogenic emissions exhibit symbiotic and explosive behavior | 10.1016/j.isci.2022.104741 | https://doi.org/10.1016/j.isci.2022.104741 | iScience | 2,022 | Sarkodie, S.; Owusu, P. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Seasonal challenges for a California renewable- energy-driven grid | 10.1016/j.isci.2021.103577 | https://doi.org/10.1016/j.isci.2021.103577 | iScience | 2,022 | Abido, M.; Mahmud, Z.; Sánchez-Pérez, P.; Kurtz, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Targeted demand response for mitigating price volatility and enhancing grid reliability in synthetic Texas electricity markets | 10.1016/j.isci.2021.103723 | https://doi.org/10.1016/j.isci.2021.103723 | iScience | 2,022 | Lee, K.; Geng, X.; Sivaranjani, S.; Xia, B.; Ming, H. | CrossRef | FLEXERGY | Demand Response | Carbon Trading & New Business Models | Optimization & Control | ||
Changing sensitivity to cold weather in Texas power demand | 10.1016/j.isci.2022.104173 | https://doi.org/10.1016/j.isci.2022.104173 | iScience | 2,022 | Shaffer, B.; Quintero, D.; Rhodes, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Distribution grid impacts of electric vehicles: A California case study | 10.1016/j.isci.2021.103686 | https://doi.org/10.1016/j.isci.2021.103686 | iScience | 2,022 | Jenn, A.; Highleyman, J. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Planning for the evolution of the electric grid with a long-run marginal emission rate | 10.1016/j.isci.2022.103915 | https://doi.org/10.1016/j.isci.2022.103915 | iScience | 2,022 | Gagnon, P.; Cole, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Aqueous zinc batteries: Design principles toward organic cathodes for grid applications | 10.1016/j.isci.2022.104204 | https://doi.org/10.1016/j.isci.2022.104204 | iScience | 2,022 | Grignon, E.; Battaglia, A.; Schon, T.; Seferos, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Large balancing areas and dispersed renewable investment enhance grid flexibility in a renewable-dominant power system in China | 10.1016/j.isci.2022.103749 | https://doi.org/10.1016/j.isci.2022.103749 | iScience | 2,022 | Lin, J.; Abhyankar, N.; He, G.; Liu, X.; Yin, S. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Heterogeneous changes in electricity consumption patterns of residential distributed solar consumers due to battery storage adoption | 10.1016/j.isci.2022.104352 | https://doi.org/10.1016/j.isci.2022.104352 | iScience | 2,022 | Qiu, Y.; Xing, B.; Patwardhan, A.; Hultman, N.; Zhang, H. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Novel Low/Zero Carbon Technologies | Demand Response & IoT | ||
Uncovering the biological basis of control energy: Structural and metabolic correlates of energy inefficiency in temporal lobe epilepsy | 10.1126/sciadv.abn2293 | https://doi.org/10.1126/sciadv.abn2293 | Science Advances | 2,022 | He, X.; Caciagli, L.; Parkes, L.; Stiso, J.; Karrer, T. | Network control theory is increasingly used to profile the brain’s energy landscape via simulations of neural dynamics. This approach estimates the control energy required to simulate the activation of brain circuits based on structural connectome measured using diffusion magnetic resonance imaging, thereby quantifying those circuits’ energetic efficiency. The biological basis of control energy, however, remains unknown, hampering its further application. To fill this gap, investigating temporal | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Addressing gain-bandwidth trade-off by a monolithically integrated photovoltaic transistor | 10.1126/sciadv.abq0187 | https://doi.org/10.1126/sciadv.abq0187 | Science Advances | 2,022 | Li, Y.; Chen, G.; Zhao, S.; Liu, C.; Zhao, N. |
The gain-bandwidth trade-off limits the development of high-performance photodetectors; i.e., the mutual restraint between the response speed and gain has intrinsically limited performance optimization of photomultiplication phototransistors and photodiodes. Here, we show that a monolithically integrated photovoltaic transistor can solve this dilemma. In this structure, the photovoltage generated by the superimposed perovskite solar cell, acting as a float gate, is amplified by the | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Influence of voids on the thermal and light stability of perovskite solar cells | 10.1126/sciadv.abo5977 | https://doi.org/10.1126/sciadv.abo5977 | Science Advances | 2,022 | Wang, M.; Fei, C.; Uddin, M.; Huang, J. | The formation of voids in perovskite films close to the buried interface has been reported during film deposition. These voids are thought to limits the efficiency and stability of perovskite solar cells (PSCs). Here, we studied the voids formed during operation in perovskite films that were optimized during the solution deposition process to avoid voids. New voids formed during operation are found to assemble along grain boundaries at the bottom interface, caused by the loss of residual solvent | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
The effect of renewable energy incorporation on power grid stability and resilience | 10.1126/sciadv.abj6734 | https://doi.org/10.1126/sciadv.abj6734 | Science Advances | 2,022 | Smith, O.; Cattell, O.; Farcot, E.; O’Dea, R.; Hopcraft, K. | Contemporary proliferation of renewable power generation is causing an overhaul in the topology, composition, and dynamics of electrical grids. These low-output, intermittent generators are widely distributed throughout the grid, including at the household level. It is critical for the function of modern power infrastructure to understand how this increasingly distributed layout affects network stability and resilience. This paper uses dynamical models, household power consumption, and photovolt | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Safer carbon nanotube processing expands industrial and consumer applications | 10.1126/sciadv.abq4853 | https://doi.org/10.1126/sciadv.abq4853 | Science Advances | 2,022 | Lowery, J.; Green, M. | Safer, less-reactive superacid processing enables printing and coating of carbon nanotubes into films, fibers, and fabrics. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
A synthetic building operation dataset | 10.1038/s41597-021-00989-6 | https://doi.org/10.1038/s41597-021-00989-6 | Scientific Data | 2,021 | Li, H.; Wang, Z.; Hong, T. | AbstractThis paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicag | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany | 10.1038/s41597-021-00963-2 | https://doi.org/10.1038/s41597-021-00963-2 | Scientific Data | 2,021 | Wenninger, M.; Maier, A.; Schmidt, J. | AbstractReal-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
An open tool for creating battery-electric vehicle time series from empirical data, emobpy | 10.1038/s41597-021-00932-9 | https://doi.org/10.1038/s41597-021-00932-9 | Scientific Data | 2,021 | Gaete-Morales, C.; Kramer, H.; Schill, W.; Zerrahn, A. | AbstractThere is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physic | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Time series of useful energy consumption patterns for energy system modeling | 10.1038/s41597-021-00907-w | https://doi.org/10.1038/s41597-021-00907-w | Scientific Data | 2,021 | Priesmann, J.; Nolting, L.; Kockel, C.; Praktiknjo, A. | AbstractThe analysis of energy scenarios for future energy systems requires appropriate data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce. In our JERICHO-E-usage dataset, we provide comprehensive data on useful energy consumption patterns for heat, cold, mechanical energy, information and communication, and light in high spatial and temporal resolution. Furthermore, we distinguish between residential, i | CrossRef | DigiEnergy | Renewable Energy Simulation Tools | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Truck electrification has minor grid impacts | 10.1038/s41560-021-00857-y | https://doi.org/10.1038/s41560-021-00857-y | Nature Energy | 2,021 | Liimatainen, H. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Cleaning cars, grid and air | 10.1038/s41560-020-00769-3 | https://doi.org/10.1038/s41560-020-00769-3 | Nature Energy | 2,021 | Smith, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Inequality built into the grid | 10.1038/s41560-021-00873-y | https://doi.org/10.1038/s41560-021-00873-y | Nature Energy | 2,021 | Moreno-Munoz, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Inequitable access to distributed energy resources due to grid infrastructure limits in California | 10.1038/s41560-021-00887-6 | https://doi.org/10.1038/s41560-021-00887-6 | Nature Energy | 2,021 | Brockway, A.; Conde, J.; Callaway, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | ||
Economic, environmental and grid-resilience benefits of converting diesel trains to battery-electric | 10.1038/s41560-021-00915-5 | https://doi.org/10.1038/s41560-021-00915-5 | Nature Energy | 2,021 | Popovich, N.; Rajagopal, D.; Tasar, E.; Phadke, A. | Abstract
Nearly all US locomotives are propelled by diesel-electric drives, which emit 35 million tonnes of CO
2
and produce air pollution causing about 1,000 premature deaths annually, accounting for approximately US$6.5 billion in annual health damage costs. Improved battery technology plus access to cheap renewable electricity open the possibility of battery-electric rail. Here we show that a 241-km range can be ac | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation | 10.1038/s41467-021-25720-2 | https://doi.org/10.1038/s41467-021-25720-2 | Nature Communications | 2,021 | Joshi, S.; Mittal, S.; Holloway, P.; Shukla, P.; Ó Gallachóir, B. | AbstractRooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2of global land s | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Demand Response & IoT | |
Linear reinforcement learning in planning, grid fields, and cognitive control | 10.1038/s41467-021-25123-3 | https://doi.org/10.1038/s41467-021-25123-3 | Nature Communications | 2,021 | Piray, P.; Daw, N. | Abstract
It is thought that the brain’s judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control |
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