Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition

• URL: http://arxiv.org/abs/2402.11664v1
• Date: Sun, 18 Feb 2024 17:55:59 GMT
• Title: Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition
• Authors: Yuqi Jiang, Yan Li, and Yize Chen
• Abstract summary: This paper proposes an interpretable deep learning method, which learns a linear combination of neural networks that each attends to an input time feature. Case studies have been carried out on the Belgium central grid load dataset and the proposed model demonstrated better accuracy compared to the frequently applied baseline model.
• Score: 3.080999981940039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid progress in machine learning and deep learning has enabled a wide range of applications in the electricity load forecasting of power systems, for instance, univariate and multivariate short-term load forecasting. Though the strong capabilities of learning the non-linearity of the load patterns and the high prediction accuracy have been achieved, the interpretability of typical deep learning models for electricity load forecasting is less studied. This paper proposes an interpretable deep learning method, which learns a linear combination of neural networks that each attends to an input time feature. We also proposed a multi-scale time series decomposition method to deal with the complex time patterns. Case studies have been carried out on the Belgium central grid load dataset and the proposed model demonstrated better accuracy compared to the frequently applied baseline model. Specifically, the proposed multi-scale temporal decomposition achieves the best MSE, MAE and RMSE of 0.52, 0.57 and 0.72 respectively. As for interpretability, on one hand, the proposed method displays generalization capability. On the other hand, it can demonstrate not only the feature but also the temporal interpretability compared to other baseline methods. Besides, the global time feature interpretabilities are also obtained. Obtaining global feature interpretabilities allows us to catch the overall patterns, trends, and cyclicality in load data while also revealing the significance of various time-related features in forming the final outputs.

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