Simplicity in dynamic and competitive electricity markets: A case study on enhanced linear models versus complex deep-learning models for day-ahead electricity price forecasting

In the transitioning electricity market of China, accurate forecasting of Day-Ahead Electricity Prices (DAEP) is crucial for strategic planning and profit optimization of market participants. It plays a significant role in resource allocation and in enhancing the efficiency of the energy system. DAEP forecasting in complex electricity markets is challenging due to a multitude of factors, including end-user consumption patterns and physical elements like network losses and transmission congestion. Furthermore, DAEP bidding strategies are often entwined with strategic gaming behavior. Motivated by this, we introduce a novel enhanced linear framework designed to optimize the trade-off between preserving historical patterns (the memory function) and extending predictions to new situations (the generalization function) in DAEP forecasting. The framework employs a linear network to capture data trends and Multi-Layer Perceptron networks for the robust extraction of intricate features and generalization. The proposed enhanced linear framework is developed and evaluated using real-world data from 3 geographically distinct power plants in Guangdong, the province with the highest economic scale and electricity consumption in China. Our approach outperforms representative deep-learning methods, including the Long Short-Term Memory model and Transformer models, with improvements of RMSE up to 26.64% and 51.80%, respectively. Additionally, the results reveal that complex models do not always outperform more straightforward ones in real-world markets characterized by extensive interaction and competition. This indicates the proposed framework provides a straightforward but effective method for time-series DAEP forecasting within the competitive electricity markets. Accurate DAEP forecasting can enhance grid security, facilitate optimal resource allocation, and promote the integration of green and low-carbon power sources into the urban energy system. © 2025 Elsevier Ltd