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Design of a hybrid learning model for establishing consistency in smart grid environment | 10.1038/s41598-025-28986-4 | https://doi.org/10.1038/s41598-025-28986-4 | Scientific Reports | 2,025 | Mahendran, S.; Gomathy, B. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Improving efficiency in smart grid monitoring using hybrid classification and dimensionality reduction | 10.1038/s41598-025-26009-w | https://doi.org/10.1038/s41598-025-26009-w | Scientific Reports | 2,025 | Kumar, T.; Kesavaraja, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Robust algorithm development of frequency estimation in smart grid | 10.1038/s41598-025-16533-0 | https://doi.org/10.1038/s41598-025-16533-0 | Scientific Reports | 2,025 | Yu, Y.; Yang, Y.; Wang, X.; Lv, L.; Chen, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
A smart grid data sharing scheme supporting policy update and traceability | 10.1038/s41598-025-10704-9 | https://doi.org/10.1038/s41598-025-10704-9 | Scientific Reports | 2,025 | Yang, X.; Yao, K.; Li, S.; Du, X.; Wang, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme | 10.1038/s41598-025-02690-9 | https://doi.org/10.1038/s41598-025-02690-9 | Scientific Reports | 2,025 | Ali, M.; Besheer, A.; Emara, H.; Bahgat, A. | Abstract
The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. This paper introduces a novel cost–benefit approach for scheduling battery energy storage systems (BESS) within microgrids (MGs) that features smart grid attributes... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
Artificial intelligence-augmented smart grid architecture for cyber intrusion detection and mitigation in electric vehicle charging infrastructure | 10.1038/s41598-025-04984-4 | https://doi.org/10.1038/s41598-025-04984-4 | Scientific Reports | 2,025 | Sharma, A.; Rani, S.; Shabaz, M. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Interpretable adaptive fault detection method for smart grid based on belief rule base | 10.1038/s41598-025-91897-x | https://doi.org/10.1038/s41598-025-91897-x | Scientific Reports | 2,025 | Li, Y.; Bai, Y.; Yang, R.; Feng, Z.; He, W. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Identification and suppression of low-frequency oscillations using PMU measurements based power system model in smart grid | 10.1038/s41598-025-88389-3 | https://doi.org/10.1038/s41598-025-88389-3 | Scientific Reports | 2,025 | Zuhaib, M.; Rihan, M.; Gupta, S.; Sufyan, M. | Abstract
Low-frequency oscillations (LFO) are inherent to large interconnected power systems. Timely detection and mitigation of these oscillations is essential to maintain reliable power system operation. This paper presents a methodology to identify and mitigate low-frequency oscillations ( forced and inter... | CrossRef | DigiEnergy | Renewable Energy Simulation Tools | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Effect of thermal stress on the life of DC link capacitors for smart grid | 10.1038/s41598-025-88522-2 | https://doi.org/10.1038/s41598-025-88522-2 | Scientific Reports | 2,025 | Sun, X.; Qiao, Y.; Li, Y.; Cao, C.; Guo, X. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Flexible renewable integrated energy system capabilities to improve voltage stability with power quality and economic environmental operation of smart grid | 10.1038/s41598-025-29052-9 | https://doi.org/10.1038/s41598-025-29052-9 | Scientific Reports | 2,025 | Hassankashi, A.; Dini, A.; Pirouzi, S.; Veisi, M.; Bahreini, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Novel machine learning approach for enhanced smart grid power use and price prediction using advanced shark Smell-Tuned flexible support vector machine | 10.1038/s41598-025-05083-0 | https://doi.org/10.1038/s41598-025-05083-0 | Scientific Reports | 2,025 | Duan, Y.; Xu, Z.; Chen, H.; Wang, Y. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
AI-driven smart grid optimization for hospital energy systems integrating renewable generation, predictive maintenance, and resilient infrastructure | 10.1038/s41598-025-28907-5 | https://doi.org/10.1038/s41598-025-28907-5 | Scientific Reports | 2,025 | Sarker, M.; Ramasamy, G.; Al Qwaid, M.; Hossen, M.; Sadeque, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method | 10.1038/s41598-025-18105-8 | https://doi.org/10.1038/s41598-025-18105-8 | Scientific Reports | 2,025 | Gul, U.; Raza Ur Rehman, H.; Gul, M.; Mezquita, G.; Barrera, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Prediction of electricity consumption and hydropower production in the smart power grid based on the gated recurrent unit neural network and modified future search algorithm | 10.1038/s41598-025-32294-2 | https://doi.org/10.1038/s41598-025-32294-2 | Scientific Reports | 2,025 | Tang, H.; Wang, Y.; Yuan, X.; Razmjooy, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Resilient cybersecurity in smart grid ICS communication using BLAKE3-driven dynamic key rotation and intrusion detection | 10.1038/s41598-025-17530-z | https://doi.org/10.1038/s41598-025-17530-z | Scientific Reports | 2,025 | Dokku, N.; David Amar Raj, R.; Bodapati, S.; Pallakonda, A.; Reddy, Y. | Abstract
The increasing convergence of Industrial Control Systems (ICS) with critical infrastructure, such as smart grids, has increased their exposure to advanced cyber threats, demanding advanced security frameworks to maintain security and operational integrity. This paper shows an innovative cybersecurity... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Grid tied hybrid PV fuel cell system with energy storage and ANFIS based MPPT for smart EV charging | 10.1038/s41598-025-09626-3 | https://doi.org/10.1038/s41598-025-09626-3 | Scientific Reports | 2,025 | vendoti, S.; Tulasi, N.; Jalli, R.; Ponnuru, S.; Jin, Z. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Multi objective moth swarm algorithm for optimizing electric vehicle integration in distribution grids | 10.1038/s41598-025-10849-7 | https://doi.org/10.1038/s41598-025-10849-7 | Scientific Reports | 2,025 | Azadikhouy, M. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Multi-objective route optimization for electric vehicle hazardous materials transportation in uncertain environments | 10.1038/s41598-025-32134-3 | https://doi.org/10.1038/s41598-025-32134-3 | Scientific Reports | 2,025 | Zhang, Q.; Zhang, Z.; Ma, C. | Abstract
This paper focuses on the application of electric vehicles in the transportation of Category 9 hazardous materials. Given the high requirements for safety and timeliness in hazardous materials transportation, this study first comprehensively considers the impacts of population density uncerta... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Carbon management in massive electric vehicle temporal and spatial scheduling with automotive electronic forensics | 10.1038/s41598-025-93798-5 | https://doi.org/10.1038/s41598-025-93798-5 | Scientific Reports | 2,025 | Cao, Y.; Zhang, Y.; Zhao, C. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Survival analysis of electric vehicle charging behavior and the temporal evolution of feature effects | 10.1038/s41598-025-18771-8 | https://doi.org/10.1038/s41598-025-18771-8 | Scientific Reports | 2,025 | Meža, M.; Strle, G.; Meža, M. | Abstract
This study proposes a survival-based modeling framework that combines behavioral features with interpretable machine learning to understand and predict user churn in electric vehicle charging services. Using a dataset of 1,074 users and 107,531 charging sessions from Central European countries, we mo... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | AI & Deep Learning | |
Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle | 10.1038/s41598-025-11330-1 | https://doi.org/10.1038/s41598-025-11330-1 | Scientific Reports | 2,025 | Paulraj, T.; Obulesu, Y. | Abstract
This research introduces a novel machine learning-based strategy for generating supercapacitor (SC) reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). A Long Short-Term Memory (LSTM) neural network is trained using real-wo... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Location allocation and capacity optimization for a PV and battery integrated hybrid community electric vehicle charging station | 10.1038/s41598-025-31865-7 | https://doi.org/10.1038/s41598-025-31865-7 | Scientific Reports | 2,025 | Kayal, P.; Braciník, P. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Smart Strategies for Improving Electric Vehicle Battery Performance and Efficiency | 10.1038/s41598-025-25987-1 | https://doi.org/10.1038/s41598-025-25987-1 | Scientific Reports | 2,025 | Tangi, S.; Vatsa, A.; Opam, A.; Bonthagorla, P.; Gaonkar, D. | Abstract
The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and veloc... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
New flexible bidirectional converter for electric vehicle substations connecting microgrids | 10.1038/s41598-025-19277-z | https://doi.org/10.1038/s41598-025-19277-z | Scientific Reports | 2,025 | Vinh, N.; Nguyen, V.; Van Dung, N.; Vu, H. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Strategic design of wind energy and battery storage for efficient and sustainable energy systems | 10.1038/s41598-025-18863-5 | https://doi.org/10.1038/s41598-025-18863-5 | Scientific Reports | 2,025 | Eroğlu, H.; Kurtuluş, O. | Abstract
The intermittent nature of renewable energy sources, particularly wind power, necessitates advanced energy management and storage strategies to ensure grid stability and economic viability. This study investigates the techno economic benefits of integrating Battery Energy Storage Systems (BESS) into ... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Interval-aware optimal control of PMSG-based wind energy conversion systems via piecewise Chebyshev inclusion | 10.1038/s41598-025-26563-3 | https://doi.org/10.1038/s41598-025-26563-3 | Scientific Reports | 2,025 | Razmjooy, N. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Optimizing weak grid integrated wind energy systems using ANFIS-SRF controlled DSTATCOM | 10.1038/s41598-025-98872-6 | https://doi.org/10.1038/s41598-025-98872-6 | Scientific Reports | 2,025 | Ramana, P.; Rosalina, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
DDPG algorithm for power optimization and control of solar PV-integrated DFIG wind energy systems | 10.1038/s41598-025-19818-6 | https://doi.org/10.1038/s41598-025-19818-6 | Scientific Reports | 2,025 | Pandey, R.; Bose, S.; Dwivedi, P. | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | ||
Reducing power ripple for multi-rotor wind energy systems using FOPDPI controllers | 10.1038/s41598-025-96625-z | https://doi.org/10.1038/s41598-025-96625-z | Scientific Reports | 2,025 | Benbouhenni, H.; Colak, I.; Elbarbary, Z.; Irshad, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Predictive riccati control for enhancing power quality using extended pq theory in wind energy-based conversion systems | 10.1038/s41598-025-13782-x | https://doi.org/10.1038/s41598-025-13782-x | Scientific Reports | 2,025 | Sundari, K.; Umamaheswari, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Design of a distributed power system using solar PV and micro turbine-based wind energy system with a flywheel energy storage | 10.1038/s41598-025-29604-z | https://doi.org/10.1038/s41598-025-29604-z | Scientific Reports | 2,025 | Bhavani, T.; Rajababu, D.; Irfan, M.; Rakesh, T.; Sekhar, P. | Abstract
As renewable energy sources gain distinction in distributed power generation, micro-grid systems integrating solar photovoltaic (PV), micro-turbine-based wind energy, and flywheel energy storage have developed as sustainable solutions. This paper presents a novel design methodology for a hybr... | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
Bi-objective operation optimization of regional integrated energy system considering shared energy storage | 10.1038/s41598-025-22502-4 | https://doi.org/10.1038/s41598-025-22502-4 | Scientific Reports | 2,025 | Li, X.; Zhu, L.; Zhao, L.; Zhang, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Multi-timescale optimization scheduling of integrated energy systems oriented towards generalized energy storage services | 10.1038/s41598-025-92601-9 | https://doi.org/10.1038/s41598-025-92601-9 | Scientific Reports | 2,025 | Mao, Y.; Cai, Z.; Jiao, X.; Long, D. | Abstract
This paper addresses the limitations of existing research that focuses on single-sided resources and two-timescale optimization, overlooking the coordinated response of various energy storage resources across different timescales in comprehensive energy systems. To tackle these shortcomings, the stud... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Optimal scheduling of integrated energy system with gas–liquid phase change carbon dioxide energy storage considering multi-layer low-carbon benefits | 10.1038/s41598-025-05438-7 | https://doi.org/10.1038/s41598-025-05438-7 | Scientific Reports | 2,025 | Li, W.; An, G.; Cai, T.; Yang, Q. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Artificial intelligence powered intelligent energy management framework for hydrogen storage and dispatch in smart microgrids | 10.1038/s41598-025-24408-7 | https://doi.org/10.1038/s41598-025-24408-7 | Scientific Reports | 2,025 | Hassan, M. | Abstract
Hydrogen energy storage is increasingly recognized as a key enabler for enhancing flexibility and reliability in smart microgrids with high shares of renewable energy. However, its practical deployment remains constrained by challenges such as real-time dispatch complexit... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | |
Deep neural network-enhanced prediction and carbon footprint analysis of early-age high-performance manufactured sand concrete’s stress–strain behavior | 10.1038/s41598-025-89016-x | https://doi.org/10.1038/s41598-025-89016-x | Scientific Reports | 2,025 | Han, L.; Pu, G.; Guo, Q.; Shi, D.; Liu, B. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | ||
Energy consumption prediction of PEVs incorporating traffic flow information | 10.1038/s41598-025-05098-7 | https://doi.org/10.1038/s41598-025-05098-7 | Scientific Reports | 2,025 | Chen, Y.; Song, Z.; Chen, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Optimization of mine ventilation energy consumption based on improved dung beetle algorithm | 10.1038/s41598-025-15263-7 | https://doi.org/10.1038/s41598-025-15263-7 | Scientific Reports | 2,025 | Bingyan, G.; Zhe, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm | 10.1038/s41598-025-87013-8 | https://doi.org/10.1038/s41598-025-87013-8 | Scientific Reports | 2,025 | Cao, Y.; Yu, J.; Zhong, R.; Munetomo, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency | 10.1038/s41598-025-14423-z | https://doi.org/10.1038/s41598-025-14423-z | Scientific Reports | 2,025 | Mochurad, L.; Levkovych, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Deep learning approach to energy consumption modeling in wastewater pumping systems | 10.1038/s41598-025-23158-w | https://doi.org/10.1038/s41598-025-23158-w | Scientific Reports | 2,025 | Piri, J.; Masoudi, B.; Haghighi, M.; Kisi, O. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Natural gas bi-level demand response strategies considering incentives and complexities under dynamic pricing | 10.1038/s41598-025-11893-z | https://doi.org/10.1038/s41598-025-11893-z | Scientific Reports | 2,025 | Zeng, H.; Zhou, J.; Dai, H. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Demand Response & IoT | ||
An optimized demand response framework for enhancing power system reliability under wind power and EV-induced uncertainty | 10.1038/s41598-025-05482-3 | https://doi.org/10.1038/s41598-025-05482-3 | Scientific Reports | 2,025 | Pakbin, H.; Karimi, A.; Hassanzadeh, M. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Demand Response & IoT | ||
Optimizing microgrid performance a multi-objective strategy for integrated energy management with hybrid sources and demand response | 10.1038/s41598-025-00118-y | https://doi.org/10.1038/s41598-025-00118-y | Scientific Reports | 2,025 | Moosavi, M.; Olamaei, J.; Shourkaei, H. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss | 10.1038/s41598-024-82379-7 | https://doi.org/10.1038/s41598-024-82379-7 | Scientific Reports | 2,025 | Loji, K.; Sharma, S.; Sharma, G.; Rawat, T. | AbstractIn this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (L... | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Optimization & Control | |
An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system | 10.1038/s41598-025-85175-z | https://doi.org/10.1038/s41598-025-85175-z | Scientific Reports | 2,025 | Dey, B.; Sharma, G.; Bokoro, P.; Dutta, S. | AbstractThe cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constra... | CrossRef | FLEXERGY | Demand Response | Carbon Trading & New Business Models | Forecasting & Prediction | |
Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response | 10.1038/s41598-025-88776-w | https://doi.org/10.1038/s41598-025-88776-w | Scientific Reports | 2,025 | Xiao, G.; Liu, H.; Nabatalizadeh, J. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program | 10.1038/s41598-024-83284-9 | https://doi.org/10.1038/s41598-024-83284-9 | Scientific Reports | 2,025 | Elshenawy, M.; Mohamed, A.; Ali, A.; Mosa, M. | AbstractTo improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy ma... | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | |
Analyzing the impacts of employing demand response and creating optimal coalition on optimal scheduling of multi-microgrid | 10.1038/s41598-025-95863-5 | https://doi.org/10.1038/s41598-025-95863-5 | Scientific Reports | 2,025 | Altimania, M.; Rostami , R.; Hosseinnia, H.; Alromithy, F. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Day-ahead economic dispatch of wind-integrated microgrids using coordinated energy storage and hybrid demand response strategies | 10.1038/s41598-025-11561-2 | https://doi.org/10.1038/s41598-025-11561-2 | Scientific Reports | 2,025 | Meng, Q.; He, Y.; Hussain, S.; Lu, J.; Guerrero, J. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Multi-objective optimization of gamified demand response for PV-integrated microgrids: a novel NSGA-III framework with behavioral adaptation modeling | 10.1038/s41598-025-13904-5 | https://doi.org/10.1038/s41598-025-13904-5 | Scientific Reports | 2,025 | Duan, Y.; Gao, C.; Zhang, J.; Wu, Y.; Zhou, S. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency | 10.1038/s41598-025-86232-3 | https://doi.org/10.1038/s41598-025-86232-3 | Scientific Reports | 2,025 | Singh, A.; Dey, B.; Bajaj, M.; Kadiwala, S.; Kumar, R. | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Grid connection barriers to renewable energy deployment in the United States | 10.1016/j.joule.2024.11.008 | https://doi.org/10.1016/j.joule.2024.11.008 | Joule | 2,025 | Gorman, W.; Kemp, J.; Rand, J.; Seel, J.; Wiser, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Dynamics of disordered intermediates during the two-electron alkaline MnO2 conversion reaction for grid-scale batteries | 10.1016/j.joule.2025.102090 | https://doi.org/10.1016/j.joule.2025.102090 | Joule | 2,025 | Zimmerer, E.; Liang, W.; Somaskandan, R.; DeToma, E.; Fawcett, C. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
The greenhouse gas burden of weatherizing northern US homes and estimated lifetime energy savings | 10.1016/j.crsus.2025.100550 | https://doi.org/10.1016/j.crsus.2025.100550 | Cell Reports Sustainability | 2,025 | Pfadt-Trilling, A.; Fortier, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Controlling outsourced emissions matters for decarbonizing China’s urban wastewater sector at all life-cycle stages | 10.1016/j.crsus.2025.100323 | https://doi.org/10.1016/j.crsus.2025.100323 | Cell Reports Sustainability | 2,025 | Zhang, L.; Liu, B.; Chen, B.; Chen, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Real-time greenhouse gas emission intensity informed demand-side load regulation for power grid decarbonization | 10.1016/j.crsus.2025.100367 | https://doi.org/10.1016/j.crsus.2025.100367 | Cell Reports Sustainability | 2,025 | Li, H.; Shen, G.; Senemmar, S.; Mehmani, A.; Zhang, J. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Spatiotemporal carbon footprints of electricity production and consumption in China | 10.1016/j.crsus.2025.100466 | https://doi.org/10.1016/j.crsus.2025.100466 | Cell Reports Sustainability | 2,025 | Li, Z.; Li, J.; Cao, Z.; Hu, K.; Ao, R. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | ||
Electric vehicle greenhouse gas benefits considering grid emission allocation and charging scheduling | 10.1016/j.crsus.2025.100486 | https://doi.org/10.1016/j.crsus.2025.100486 | Cell Reports Sustainability | 2,025 | Arowosola, A.; Norris, G.; Kirchain, R.; De Kleine, R.; Kim, H. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Toward understanding the complexity of long-duration energy storage siting in high renewable power grids | 10.1016/j.isci.2025.112571 | https://doi.org/10.1016/j.isci.2025.112571 | iScience | 2,025 | Cole, D.; Dalvi, S.; Zavala, V.; Guerra, O. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Drawing power from a patchwork: Harnessing a decentralized electricity grid | 10.1016/j.isci.2025.113230 | https://doi.org/10.1016/j.isci.2025.113230 | iScience | 2,025 | Heard, B.; Holmes, K. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Assessing the techno-economic benefits of LEMs for different grid topologies and prosumer shares | 10.1016/j.isci.2025.112493 | https://doi.org/10.1016/j.isci.2025.112493 | iScience | 2,025 | Doepfert, M.; Candas, S.; Kraus, H.; Tzscheutschler, P.; Hamacher, T. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Spatiotemporal planning of electric vehicle charging infrastructure: Demand estimation and grid-aware optimization under uncertainty | 10.1016/j.isci.2025.113368 | https://doi.org/10.1016/j.isci.2025.113368 | iScience | 2,025 | Wang, J.; Kaushik, H.; Jacob, R.; Zhang, J. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Earth Grid: Toward a low-carbon energy infrastructure | 10.1016/j.isci.2025.113681 | https://doi.org/10.1016/j.isci.2025.113681 | iScience | 2,025 | Kumar, A.; HE, X.; Deng, Y.; Sah, B.; Singh, A. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Coordinated multi-objective optimization scheduling for electric vehicle swapping station cluster and grid | 10.1016/j.isci.2025.112444 | https://doi.org/10.1016/j.isci.2025.112444 | iScience | 2,025 | Liao, X.; Zheng, Z.; Qian, B.; Wang, H.; Zhan, D. | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Optimization & Control | ||
Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction | 10.1016/j.isci.2025.112360 | https://doi.org/10.1016/j.isci.2025.112360 | iScience | 2,025 | Mai, C.; Zhang, L.; Behar, O.; Hu, X.; Chao, X. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Deciphering decarbonization trajectories in China by spatiotemporal-accumulation modeling of electricity carbon footprint | 10.1016/j.isci.2025.111963 | https://doi.org/10.1016/j.isci.2025.111963 | iScience | 2,025 | Tang, J.; Shan, R.; Wang, P.; Chen, W.; Gu, D. | CrossRef | EnergiTrade | Urban Carbon Footprint | Carbon Trading & New Business Models | Demand Response & IoT | ||
A hybrid demand-side policy for balanced economic emission in microgrid systems | 10.1016/j.isci.2025.112121 | https://doi.org/10.1016/j.isci.2025.112121 | iScience | 2,025 | Singh, A.; Dey, B.; Misra, S.; Kumar, R.; Bajaj, M. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | ||
Soil carbon formation is promoted by saturation deficit and existing mineral-associated carbon, not by microbial carbon-use efficiency | 10.1126/sciadv.adv9482 | https://doi.org/10.1126/sciadv.adv9482 | Science Advances | 2,025 | King, A.; Sokol, N. | Mineral-associated organic carbon (MAOC) is the largest terrestrial pool of organic carbon, yet controls on its formation remain unresolved. Existing MAOC is thought to preclude additional C storage on minerals, but this perspective is difficult to reconcile with observations that MAOC stacks in multilayers, suggesting... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Spatiotemporal toughness modulation in hydrogels through on-demand cross-linking | 10.1126/sciadv.adz0440 | https://doi.org/10.1126/sciadv.adz0440 | Science Advances | 2,025 | Lee, J.; Castilho, R.; Nam, S. |
Tough hydrogels are promising for soft robotics, bioelectronics, and tissue adhesives due to their exceptional resilience and biocompatibility, yet precise spatiotemporal control of their mechanics remains challenging. Here, we present a hydrogel platform that enables spatiotemporal modulation of toughness... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Smart ultra-long-lasting sequentially triggerable and artfully implantable nozzle system for on-demand drug delivery for chronotherapy | 10.1126/sciadv.adv8734 | https://doi.org/10.1126/sciadv.adv8734 | Science Advances | 2,025 | Zeng, Q.; Gong, Y.; Jiao, W.; Xu, J.; Chen, X. |
Conventional drug delivery methods for chronic disease often suffer from low potency and poor patient compliance, while current advanced devices face limitations because of bulkiness, frequent implantation needs, inflammation risk, and lack of precise control. To overcome these challenges, we developed the... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Experimental characterization of complex atmospheric flows: A wind turbine wake case study | 10.1126/sciadv.adw8524 | https://doi.org/10.1126/sciadv.adw8524 | Science Advances | 2,025 | Angelou, N.; Sjöholm, M.; Mikkelsen, T. | Our current understanding of the interaction between the atmosphere and surface obstacles crucial for boundary-layer meteorology, forestry, urban climate, wind engineering, and wind energy is limited mainly to observations acquired in wind tunnel experiments and flow predictions from computational fluid dynamic models.... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Flash annealing–engineered wafer-scale relaxor antiferroelectrics for enhanced energy storage performance | 10.1126/sciadv.ady2349 | https://doi.org/10.1126/sciadv.ady2349 | Science Advances | 2,025 | Li, Y.; Song, K.; Zhu, M.; Li, X.; Zeng, Z. |
Dielectric capacitors are essential for energy storage systems because of their high-power density and fast operation speed. However, optimizing energy storage density with concurrent thermal stability remains a substantial challenge. Here, we develop a flash annealing process with ultrafast heatin... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Atomic Sn–incorporated subnanopore-rich hard carbon host for highly reversible quasi-metallic Li storage | 10.1126/sciadv.ads6483 | https://doi.org/10.1126/sciadv.ads6483 | Science Advances | 2,025 | Jin, T.; Zhang, X.; Yuan, S.; Yu, L. |
The practical application of Li metal anodes has been hindered by severely irreversible side reactions for low Coulombic efficiency, uncontrollable growth of Li dendrites, and large volume change. Herein, we report subnanopore-rich carbon spheres encapsulated with Sn single atoms (Sn/CS@SC) as a Li host to... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
The potential of wastewater treatment on carbon storage through ocean alkalinity enhancement | 10.1126/sciadv.ads0313 | https://doi.org/10.1126/sciadv.ads0313 | Science Advances | 2,025 | Zheng, L.; Hu, Y.; Su, B.; Chen, Q.; Liu, J. |
Ocean alkalinity enhancement (OAE) implemented through wastewater treatment plants increases the alkalinity of the effluents and discharges them into the ocean, referred to as wastewater-based OAE. However, the alkalization capability and its carbon storage stability when adding alkaline minerals to wastew... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
A compact cassette tape for DNA-based data storage | 10.1126/sciadv.ady3406 | https://doi.org/10.1126/sciadv.ady3406 | Science Advances | 2,025 | Li, J.; Mao, C.; Wang, S.; Li, X.; Luo, X. |
DNA with high storage density can serve as an alternative storage medium to respond to the global explosion of data growth and become a powerful personal storage memory if an integrated compact device can store and handle large-scale data. Here, we incorporate a DNA cassette tape with 5.5 × 10
... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Extending tetrahedral network similarity to carbon: A type-I carbon clathrate stabilized by boron | 10.1126/sciadv.adv6867 | https://doi.org/10.1126/sciadv.adv6867 | Science Advances | 2,025 | Strobel, T.; Bi, T.; Guńka, P.; Hansen, M.; Hübner, J. |
Clathrates are guest/host framework compounds composed of polyhedral cages, yet despite their prevalence among tetrahedral network formers, clathrates with a carbon host lattice remain unrealized synthetic targets. Here, we report a type-I carbon-based framework—a ubiquitous clathrate structure type found ... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | |
County-level intensity of carbon emissions from crop farming in China during 2000–2019 | 10.1038/s41597-024-03296-y | https://doi.org/10.1038/s41597-024-03296-y | Scientific Data | 2,024 | Li, C.; Jia, J.; Wu, F.; Zuo, L.; Cui, X. | AbstractAgriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China’s carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote s... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
Comprehensive Dataset on Electrical Load Profiles for Energy Community in Ireland | 10.1038/s41597-024-03454-2 | https://doi.org/10.1038/s41597-024-03454-2 | Scientific Data | 2,024 | Trivedi, R.; Bahloul, M.; Saif, A.; Patra, S.; Khadem, S. | AbstractThis paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active ... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece | 10.1038/s41597-024-03208-0 | https://doi.org/10.1038/s41597-024-03208-0 | Scientific Data | 2,024 | Athanasoulias, S.; Guasselli, F.; Doulamis, N.; Doulamis, A.; Ipiotis, N. | AbstractThe growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ... | CrossRef | FLEXERGY | Demand Response | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
A unified dataset for pre-processed climate indicators weighted by gridded economic activity | 10.1038/s41597-024-03304-1 | https://doi.org/10.1038/s41597-024-03304-1 | Scientific Data | 2,024 | Gortan, M.; Testa, L.; Fagiolo, G.; Lamperti, F. | AbstractAlthough high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate d... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Wind turbine condition monitoring dataset of Fraunhofer LBF | 10.1038/s41597-024-03934-5 | https://doi.org/10.1038/s41597-024-03934-5 | Scientific Data | 2,024 | Mostafavi, A.; Friedmann, A. | AbstractFraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have bee... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |
SDWPF: A Dataset for Spatial Dynamic Wind Power Forecasting over a Large Turbine Array | 10.1038/s41597-024-03427-5 | https://doi.org/10.1038/s41597-024-03427-5 | Scientific Data | 2,024 | Zhou, J.; Lu, X.; Xiao, Y.; Tang, J.; Su, J. | AbstractWind power is a clean and renewable energy, yet it poses integration challenges to the grid due to its variable nature. Thus, Wind Power Forecasting (WPF) is crucial for its successful integration. However, existing WPF datasets often cover only a limited number of turbines and lack detailed information. To bri... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Forecasting & Prediction | |
Remotely sensed above-ground storage tank dataset for object detection and infrastructure assessment | 10.1038/s41597-023-02780-1 | https://doi.org/10.1038/s41597-023-02780-1 | Scientific Data | 2,024 | Robinson, C.; Bradbury, K.; Borsuk, M. | AbstractRemotely sensed imagery has increased dramatically in quantity and public availability. However, automated, large-scale analysis of such imagery is hindered by a lack of the annotations necessary to train and test machine learning algorithms. In this study, we address this shortcoming with respect to above-grou... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | |
Assessing the emissions impact of grid-connected hydrogen production | 10.1038/s41560-023-01445-y | https://doi.org/10.1038/s41560-023-01445-y | Nature Energy | 2,024 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |||
The influence of additionality and time-matching requirements on the emissions from grid-connected hydrogen production | 10.1038/s41560-023-01435-0 | https://doi.org/10.1038/s41560-023-01435-0 | Nature Energy | 2,024 | Giovanniello, M.; Cybulsky, A.; Schittekatte, T.; Mallapragada, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Publisher Correction: The influence of additionality and time-matching requirements on the emissions from grid-connected hydrogen production | 10.1038/s41560-024-01475-0 | https://doi.org/10.1038/s41560-024-01475-0 | Nature Energy | 2,024 | Giovanniello, M.; Cybulsky, A.; Schittekatte, T.; Mallapragada, D. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Weather-sensitive renewable energy sources do not subject power systems to blackouts | 10.1038/s41560-024-01657-w | https://doi.org/10.1038/s41560-024-01657-w | Nature Energy | 2,024 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |||
Impacts of renewable energy resources on the weather vulnerability of power systems | 10.1038/s41560-024-01652-1 | https://doi.org/10.1038/s41560-024-01652-1 | Nature Energy | 2,024 | Zhao, J.; Li, F.; Zhang, Q. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | ||
Weather conditions linked to energy droughts in electricity systems with hydropower | 10.1038/s41560-024-01641-4 | https://doi.org/10.1038/s41560-024-01641-4 | Nature Energy | 2,024 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Forecasting & Prediction | |||
Identifying the power-grid bottlenecks responsible for cascading failures during extreme storms | 10.1038/s41560-024-01499-6 | https://doi.org/10.1038/s41560-024-01499-6 | Nature Energy | 2,024 | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |||
Increasing the resilience of the Texas power grid against extreme storms by hardening critical lines | 10.1038/s41560-023-01434-1 | https://doi.org/10.1038/s41560-023-01434-1 | Nature Energy | 2,024 | Stürmer, J.; Plietzsch, A.; Vogt, T.; Hellmann, F.; Kurths, J. | AbstractThe Texas power grid on the Gulf Coast of the United States is frequently hit by tropical cyclones (TCs) causing widespread power outages, a risk that is expected to substantially increase under global warming. Here we introduce a new approach that combines a probabilistic line failure model with a network mode... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering | 10.1038/s41560-024-01516-8 | https://doi.org/10.1038/s41560-024-01516-8 | Nature Energy | 2,024 | Harrison-Atlas, D.; Glaws, A.; King, R.; Lantz, E. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | ||
Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage | 10.1038/s41560-024-01670-z | https://doi.org/10.1038/s41560-024-01670-z | Nature Energy | 2,024 | Li, H.; Zheng, H.; Yue, T.; Xie, Z.; Yu, S. | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | AI & Deep Learning | ||
Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images | 10.1038/s41467-023-44666-1 | https://doi.org/10.1038/s41467-023-44666-1 | Nature Communications | 2,024 | Xia, P.; Zhang, L.; Min, M.; Li, J.; Wang, Y. | Abstract
Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cl... | CrossRef | CleanTech | Solar PV & Storage | Novel Low/Zero Carbon Technologies | Forecasting & Prediction | |
A multi-demand operating system underlying diverse cognitive tasks | 10.1038/s41467-024-46511-5 | https://doi.org/10.1038/s41467-024-46511-5 | Nature Communications | 2,024 | Cai, W.; Taghia, J.; Menon, V. | AbstractThe existence of a multiple-demand cortical system with an adaptive, domain-general, role in cognition has been proposed, but the underlying dynamic mechanisms and their links to cognitive control abilities are poorly understood. Here we use a probabilistic generative Bayesian model of brain circuit dynamics to... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | Carbon Trading & New Business Models | Optimization & Control | |
Unlocking the potential of biogas systems for energy production and climate solutions in rural communities | 10.1038/s41467-024-50091-9 | https://doi.org/10.1038/s41467-024-50091-9 | Nature Communications | 2,024 | Luo, T.; Shen, B.; Mei, Z.; Hove, A.; Ju, K. | Abstract
On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we ... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Demand Response & IoT | |
Altered grid-like coding in early blind people | 10.1038/s41467-024-47747-x | https://doi.org/10.1038/s41467-024-47747-x | Nature Communications | 2,024 | Sigismondi, F.; Xu, Y.; Silvestri, M.; Bottini, R. | Abstract
Cognitive maps in the hippocampal-entorhinal system are central for the representation of both spatial and non-spatial relationships. Although this system, especially in humans, heavily relies on vision, the role of visual experience in shaping the development of cognitive maps remains largel... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control | |
How grid reinforcement costs differ by the income of electric vehicle users | 10.1038/s41467-024-53644-0 | https://doi.org/10.1038/s41467-024-53644-0 | Nature Communications | 2,024 | Steinbach, S.; Blaschke, M. | AbstractThe simultaneous charging of many electric vehicles in future mobility scenarios may lead to peaks and overloads threatening grid stability. The necessary infrastructure investments vary by the number and model type of vehicles driven and the residents’ charging preferences. These attributes significantly depen... | CrossRef | FLEXERGY | Electric Vehicles & Mobility | Demand Response & New Mobilities & Urban Planning | Forecasting & Prediction | |
Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior | 10.1038/s41467-024-49703-1 | https://doi.org/10.1038/s41467-024-49703-1 | Nature Communications | 2,024 | Hermansen, E.; Klindt, D.; Dunn, B. | Abstract
Minimal experiments, such as head-fixed wheel-running and sleep, offer experimental advantages but restrict the amount of observable behavior, making it difficult to classify functional cell types. Arguably, the grid cell, and its striking periodicity, would not have been discovered without t... | CrossRef | DigiEnergy | Load Forecasting & Demand Management | AI & Data Science for Urban Energy Systems | Optimization & Control |
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