Abstract
Electricity price forecasting (EPF) is essential for energy markets stakeholders (e.g. grid operators, energy traders, policymakers) but remains challenging due to the inherent volatility and nonlinearity of price signals. Traditional statistical and deep learning (DL) models often struggle to capture complex temporal dependencies and integrate heterogeneous data effectively. While time series foundation models (TSFMs) have shown strong performance in general time series forecasting tasks, such as traffic forecasting and weather forecasting. However, their effectiveness in day-ahead EPF, particularly in volatile markets, remains underexplored. This paper presents a spike regularization strategy and evaluates a wide range of TSFMs, including Tiny Time Mixers (TTMs), MOIRAI, MOMENT, and TimesFM, against traditional statistical and DL models such as Autoregressive Integrated Moving Average (ARIMA), Long-short Term Memory (LSTM), and Convolutional Neural Network - LSTM (CNN-LSTM) using half-hourly wholesale market data with volatile trends in Singapore. Exogenous factors (e.g. weather and calendar variables) are also incorporated into models where applicable. Results demonstrate that TSFMs consistently outperform traditional approaches, achieving up to 37.4% improvement in MAPE across various evaluation settings. The findings offer practical guidance for improving forecast accuracy and decision-making in volatile electricity markets.
Abstract (translated)
电力价格预测（EPF）对于能源市场参与者（如电网运营商、能源交易员和政策制定者）至关重要，但鉴于电价信号的固有波动性和非线性特性，这一任务仍然充满挑战。传统的统计模型和深度学习（DL）模型往往难以捕捉复杂的时序依赖关系，并且在有效整合异构数据方面表现不佳。虽然时间序列基础模型（TSFMs）在一般的时间序列预测任务中表现出色，例如交通流量预测和天气预报，但在具有波动性的市场中的日前电力价格预测的有效性仍需进一步探索。 本文提出了一种尖峰正则化策略，并评估了一系列时间序列基础模型（如Tiny Time Mixers (TTMs)、MOIRAI、MOMENT 和 TimesFM），同时对比了传统的统计和深度学习模型（例如自回归移动平均模型(ARIMA)、长短期记忆网络(LSTM) 以及卷积神经网络-长短时记忆网络(CNN-LSTM)）。评估基于新加坡批发市场每半小时的具有波动性趋势的数据。当适用时，外部因素（如天气和日历变量）也被纳入到相应的模型中。 结果表明，时间序列基础模型在各种评估设置下始终优于传统方法，其中最大改进幅度达到了37.4%的平均绝对百分比误差(MAPE)降低。这些发现为提高波动性电力市场的预测准确性和决策制定提供了实用指导。
URL
https://arxiv.org/abs/2602.05430
