Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as follows: firstly, the Hampel identifier (HI) is employed to correct outliers in the original data; secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract the waveform characteristics of the data fully; and, finally, the temporal convolutional network, extreme learning machine, and gate recurrent unit are selected as the basic learners for forecasting load power data. An ensemble reinforcement learning algorithm based on Q-learning was adopted to generate optimal ensemble weights, and the predictive results of the three basic learners are combined. The experimental results of the models for three real load power datasets show that: (a) the utilization of HI improves the model’s forecasting result; (b) CEEMDAN is superior to other decomposition algorithms in forecasting performance; and (c) the proposed ensemble method, based on the Q-learning algorithm, outperforms three single models in accuracy, and achieves smaller prediction errors.

Crude oil price forecasting contributes to global economic development. This study proposes a hybrid deep learning model for crude oil price forecasting. First, empirical wavelet transform decomposes raw data into multiple. Then, three neural networks generate preliminary forecasts, which are subsequently refined by a reinforcement learning-based ensemble method. Finally, an error correction module handles residuals, further enhancing the forecasting outcomes. Three West Texas Intermediate datasets and additional emergency scenarios were used to validate the hybrid model. The findings indicate that the proposed model achieves superior predictive performance compared with sixteen benchmark methods and three advanced models.