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