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arXiv · 2412.02722

Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting

Mateusz Kasprzyk·Paweł Pełka·Boris N. Oreshkin·Grzegorz Dudek
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Abstract

This paper presents an enhanced N-BEATS model, N-BEATS*, for improved mid-term electricity load forecasting (MTLF). Building on the strengths of the original N-BEATS architecture, which excels in handling complex time series data without requiring preprocessing or domain-specific knowledge, N-BEATS* introduces two key modifications. (1) A novel loss function -- combining pinball loss based on MAPE with normalized MSE, the new loss function allows for a more balanced approach by capturing both L1 and L2 loss terms. (2) A modified block architecture -- the internal structure of the N-BEATS blocks is adjusted by introducing a destandardization component to harmonize the processing of different time series, leading to more efficient and less complex forecasting tasks. Evaluated on real-world monthly electricity consumption data from 35 European countries, N-BEATS* demonstrates superior performance compared to its predecessor and other established forecasting methods, including statistical, machine learning, and hybrid models. N-BEATS* achieves the lowest MAPE and RMSE, while also exhibiting the lowest dispersion in forecast errors.

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Mateusz Kasprzyk, Paweł Pełka, Boris N. Oreshkin, Grzegorz Dudek. 2024-12-02. Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting. https://arxiv.org/abs/2412.02722

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