A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions

TL;DR

This paper introduces a hybrid deep learning framework combining a Distilled Attention Transformer and an Autoencoder Self-regression Model to improve day-ahead electricity price forecasting, especially under extreme conditions and anomalies.

Contribution

The paper presents a novel hybrid model integrating attention mechanisms and anomaly detection for robust electricity price forecasting under extreme market conditions.

Findings

Outperforms existing methods in accuracy and robustness

Effective detection and isolation of anomalies caused by extreme events

Enhances computational efficiency in forecasting tasks

Abstract

Accurate day-ahead electricity price forecasting (DAEPF) is critical for the efficient operation of power systems, but extreme condition and market anomalies pose significant challenges to existing forecasting methods. To overcome these challenges, this paper proposes a novel hybrid deep learning framework that integrates a Distilled Attention Transformer (DAT) model and an Autoencoder Self-regression Model (ASM). The DAT leverages a self-attention mechanism to dynamically assign higher weights to critical segments of historical data, effectively capturing both long-term trends and short-term fluctuations. Concurrently, the ASM employs unsupervised learning to detect and isolate anomalous patterns induced by extreme conditions, such as heavy rain, heat waves, or human festivals. Experiments on datasets sampled from California and Shandong Province demonstrate that our framework significantly outperforms state-of-the-art methods in prediction accuracy, robustness, and computational efficiency. Our framework thus holds promise for enhancing grid resilience and optimizing market operations in future power systems.