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--- |
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license: apache-2.0 |
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metrics: |
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- mse |
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tags: |
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- Timeseries |
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- Forecasting |
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- Energy |
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datasets: |
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- shivDwd/W_LSTMix_test_dataset |
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- ai-iot/EnergyBench |
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--- |
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# W-LSTMix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting |
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**W-LSTMix** is a lightweight, modular hybrid forecasting model designed for building-level load forecasting across diverse building types. With approximately **0.13 million parameters**, W-LSTMix combines: |
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- **Wavelet-based signal decomposition** |
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- **N-BEATS** for ensemble forecasting |
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- **LSTM** for gated memory |
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- **MLP-Mixer** for efficient patch-wise mixing |
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This model achieves high forecasting accuracy with a minimal computational footprint. |
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## 🚀 Features |
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- Hybrid Architecture Combining N-BEATS, LSTM and MLP-Mixer |
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- Lightweight: ~0.13M parameters and Edge-Deployable |
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- Modular design for flexible adaptation |
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- Effective generalization across building types |
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- Zero-shot capabilities |
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## 📓 Colab Quickstart |
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Use the following steps to try W-LSTMix on Google Colab: |
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```bash |
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!git clone https://github.com/shivDwd/W-LSTMix.git |
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%cd W-LSTMix |
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!git clone https://huggingface.co/datasets/shivDwd/W_LSTMix_test_dataset |
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!pip install -r requirements.txt |
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!python test.py |
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``` |
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## 📊 Real-World Building Datasets |
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This model is trained on large-scale **real-world building energy datasets** from commercial and residential domains, collected from multiple countries. |
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| Dataset | Location | Type | # Buildings | # Observations | Years | |
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|-----------|--------------|-------------|-------------|----------------|-------------| |
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| IBlend | India | Commercial | 9 | 296,357 | 2013–2017 | |
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| Enernoc | USA | Commercial | 100 | 877,728 | 2012 | |
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| NEST | Switzerland | Residential | 1 | 34,715 | 2019–2023 | |
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| Ireland | Ireland | Residential | 20 | 174,398 | 2020 | |
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| MFRED | USA | Residential | 26 | 227,622 | 2019 | |
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| CEEW | India | Residential | 84 | 923,897 | 2019–2021 | |
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| SMART* | USA | Residential | 114 | 958,998 | 2016 | |
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| Prayas | India | Residential | 116 | 1,536,409 | 2018–2020 | |
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| NEEA | USA | Residential | 192 | 2,922,289 | 2018–2020 | |
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| SGSC | Australia | Residential | 13,735 | 172,277,213 | 2011–2014 | |
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| GoiEner | Spain | Residential | 25,559 | 632,313,933 | 2014–2022 | |
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**Total: 39,956 buildings and 812M+ hourly observations** |
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> ⚠️ These datasets are used under their respective terms/licenses for academic research only. |
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## 📈 Comparative Evaluation |
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We benchmark **W-LSTMix** against state-of-the-art Time Series Foundation Models (TSFMs) and N-BEATS under two broad settings: zero-shot and fine-tuning. |
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Please refer to the publication for a detailed summary of the results. |
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> **W-LSTMix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting** |
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> *Shivam Dwivedi, Anuj Kumar, Harish Kumar Saravanan, Pandarasamy Arjunan* |
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> In *Proceedings of the 1st ICML Workshop on Foundation Models for Structured Data, Vancouver, Canada. 2025* |
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> [https://openreview.net/pdf?id=bG04Z3Jioc](https://openreview.net/pdf?id=bG04Z3Jioc) |
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To know more about W-LSTMix, please regfer to the official [Github](https://github.com/AI-IoT-Lab/W-LSTMix.git) repository. |
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## 📄 Citation |
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If you use W-LSTMix in your research or applications, please cite our paper: |
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```bibtex |
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@inproceedings{ |
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dwivedi2025wlstmix, |
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title={W-{LSTM}ix: A Hybrid Modular Forecasting Framework for Trend and Pattern Learning in Short-Term Load Forecasting}, |
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author={SHIVAM DWIVEDI and Anuj Kumar and Harish Kumar Saravanan and Pandarasamy Arjunan}, |
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booktitle={1st ICML Workshop on Foundation Models for Structured Data}, |
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year={2025}, |
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url={https://openreview.net/forum?id=bG04Z3Jioc} |
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} |
