Energies, Vol. 18, Pages 6097: Short-Term Load Forecasting for Electricity Spot Markets Across Different Seasons Based on a Hybrid VMD-LSTM-Random Forest Model
Energies doi: 10.3390/en18236097
Authors:
Kangkang Li
Lize Yuan
Fanyue Qian
Lifei Song
Xinhong Wu
Li Wang
Jiefen Dai
Lianyi Shen
Short-term load forecasting (STLF) is a core technical support for ensuring the safe and economic operation of power systems and efficient trading in electricity spot markets. To address the limitations of traditional forecasting models in handling the strong nonlinear and non-stationary characteristics of load data under electricity spot market conditions—where load is influenced by the coupling of multiple factors, such as meteorological conditions, electricity price signals, and seasonal patterns—we propose a hybrid forecasting model (VMD-PSO-LSTM-RF) that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), Random Forest (RF), and Particle Swarm Optimization (PSO) to enhance the forecasting accuracy and market adaptability. First, VMD is applied to adaptively decompose the half-hourly power load data of a comprehensive user in Ningbo, Zhejiang Province, from July 2024 to June 2025. The original load series was decomposed into three components, effectively avoiding the mode aliasing problem common in traditional decomposition methods and providing high-quality inputs for subsequent forecasting. Simultaneously, meteorological data and temporal features were incorporated to construct a multi-dimensional input feature set, meeting the requirements of electricity spot markets for considering multiple influencing factors. Second, the PSO algorithm was used to optimize the key hyperparameters of LSTM and RF with seasonal differentiation. With the optimization, we aimed to maximize the Coefficient of Determination (R2) on the validation set, ensuring that the model parameters precisely matched the load fluctuation characteristics of each season. Finally, based on the feature differences of various frequency components, LSTM and RF were used to construct sub-models, and the final load value was obtained through weighted integration of the prediction results of each component. The results fully demonstrate that the proposed model can accurately capture the multi-scale fluctuation characteristics of load in electricity spot market environments, with forecasting performance superior to traditional single models and basic hybrid models; furthermore, the proposed model achieves precise extraction of multi-scale load features and in-depth temporal pattern mining, providing reliable technical support for efficient electricity spot market operation, as well as empirical references for formulating scenario-specific forecasting strategies and managing trading risks in electricity markets.