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Accurate short-term electricity load forecasting is crucial for day-ahead scheduling and secure operation of power systems. However, electricity load series exhibit significant non-stationarity, with complex coupling between low-frequency trends and high-frequency fluctuations, making it difficult for conventional forecasting models to simultaneously characterize the overall trend and stochastic disturbances. To address this issue, this paper proposes a short-term load forecasting model based on STL decomposition and CNN-BiLSTM optimized by deep reinforcement learning. First, the original load series is decomposed into trend, seasonal, and residual components using the STL algorithm. Second, a dual-channel parallel forecasting architecture is constructed: the linear channel uses a linear regression model to predict the trend and seasonal components, thereby characterizing the low-frequency variations in the load; the nonlinear channel uses a CNN-BiLSTM framework optimized by deep reinforcement learning to predict the high-frequency residual component, and this process is formulated as a Markov decision process. Specifically, the attention-based CNN-BiLSTM serves as the policy network, and its forecasting strategy is dynamically optimized under the guidance of a reward function to enhance the modeling capability for high-frequency stochastic fluctuations. Finally, the load forecasting results for the next 24 h are obtained through dual-channel result reconstruction. Experimental results based on the ERCOT system-level load data show that the proposed model achieves superior forecasting performance, with a root mean square error of 976.4 MW and a mean absolute percentage error of 1.81%. Further multi-season testing, meteorological perturbation analysis, fair comparison under the same STL preprocessing, and ablation experiments demonstrate that the proposed model maintains good forecasting performance under different seasonal scenarios, meteorological input errors, and fair experimental settings, thereby validating its effectiveness for short-term load forecasting.
Wang et al. (Mon,) studied this question.