Summary of Interpretable Short-term Load Forecasting Via Multi-scale Temporal Decomposition, by Yuqi Jiang et al.
Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition
by Yuqi Jiang, Yan Li, Yize Chen
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method combines neural networks that attend to input time features, offering an interpretable deep learning approach for electricity load forecasting. Building on recent advances in machine learning and deep learning, this technique excels in predicting short-term loads, demonstrating better accuracy than a frequently used baseline model. The authors also introduce a multi-scale time series decomposition method to tackle complex temporal patterns. Evaluations on the Belgium central grid load dataset yield promising results, with the proposed model achieving low mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.52, 0.57, and 0.72, respectively. Furthermore, the method displays generalization capabilities and provides both feature and temporal interpretability, allowing for a deeper understanding of load patterns, trends, and cyclicality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops an interpretable deep learning model that can accurately forecast electricity loads. The approach is designed to learn from complex time series data and identify important features. It also breaks down the data into different scales to better understand the patterns. This method was tested on a real-world dataset and performed well, with more accurate predictions than other methods. What’s special about this model is that it can explain its decisions, making it easier for experts to understand how it works. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Machine learning * Mae * Mse * Time series
