Work

Short-Term Electricity Price Forecasting via a Reinforcement Learning-Based Dynamic Soft Ensemble Strategy

Year: 2026

Type: article

Abstract: To address the high volatility of spot market prices and the feature extraction limitations of single models, a short-term electricity price forecasting method based on a reinforcement learning dynamic soft ensemble strategy is proposed. First, a complementary dual-branch architecture is constructed: the CNN-LSTM-Attention branch mines local temporal features, while the Transformer branch captures long-range global dependencies. Second, the Q-learning algorithm is introduced to model weight optimization as a Markov Decision Process. An intelligent agent perceives fluctuation states to adaptively allocate weights, overcoming the rigidity of traditional ensembles. Case studies on PJM market data demonstrate that the proposed model outperforms advanced benchmarks in MAE and RMSE metrics. Notably, prediction accuracy is significantly improved during price spikes and negative price periods. The results verify that the strategy effectively copes with market concept drift, supporting reliable bidding and risk mitigation.

Source: Energies

DOI: 10.3390/en19030761

Authors: Yan Wang, Yongxi Zhao, Kun Liang, Hong Fan

Institution: Shanghai University of Electric Power

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137