CEEMDAN-CatBoost-SATCN-based short-term load forecasting model considering time series decomposition and feature selection - Wang, Yunjia; Zhang, Zeya; Pang, Ning; Sun, Zengjie; Xu, Lixiong (Frontiers in Energy Research, 2023)
The rapidly increasing randomness and volatility of electrical power loads urge computational efficient and accurate short-term load forecasting methods for ensuring the operation efficiency and reliability of the power system. Focusing on the non-stationary and nonlinear characteristics of load curves that could easily compromise the forecasting accuracy, this paper proposes a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Catboost, and self-attention mechanism integrated temporal convolutional network (CEEMDAN-Catboost-SATCN) based short-term load forecasting method integrating time series decomposition and feature selection. CEEMDAN decomposes the original load into some periodically fluctuating components with different frequencies. With their fluctuation patterns being evaluated with permutation entropy, these components with close fluctuation patterns are further merged to improve the computational efficiency. Thereafter, Catboost based recursive feature elimination algorithm is applied to obtain the optimal feature subsets to the merged components based on feature importance, which can effectively reduce the dimension of input variables. On this basis, SATCN that consists of CNN and self-attention mechanism is proposed. The case study shows that time series decomposition and feature selection have a positive effect on improving the forecasting accuracy. Compared with other forecasting methods and evaluated with MAPE and RMSE, the proposed method outperforms in forecasting accuracy.