CEEMDAN-CatBoost-SATCN-based short-term load forecasting model considering time series decomposition and feature selection

Abstract

The rapidly increasing randomness and volatility of electrical power loads urge computationally efficient and accurate short-term load forecasting methods for ensuring the operational efficiency and reliability of the power system. Focusing on the non-stationary and non-linear characteristics of load curves that could easily compromise the forecasting accuracy, this paper proposes a complete ensemble empirical mode decomposition with adaptive noise–CatBoost–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 computational efficiency. Thereafter, a 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 which consists of a convolutional neural network and self-attention mechanism is proposed. The case study shows that time series decomposition and feature selection have a positive effect on improving forecasting accuracy. Compared with other forecasting methods and evaluated with a mean absolute percentage error and root mean square error, the proposed method outperforms in forecasting accuracy.