Short-term Power Load Forecasting Method in Rural Areas Based on CNN-LSTM

To improve the performance of rural power load forecasting, a new power load forecasting method is proposed. Firstly, basic and statistical features are extracted from historical power load data and numerical weather prediction (NWP) data. Then, t-SNE dimensionality reduction is used to extract main features, and K-means clustering is used to distinguish seasonal features. Secondly, the deep learning network CNN-LSTM (fusion of convolutional neural network and long short-term memory) is designed, and the load prediction models for spring and autumn, summer and winter are trained separately. Finally, use a rural power load in Wuxi, China to predict the power load in the next 24 hours and calculate the error. Experimental results show that the forecasting error of the proposed method is lower than that of LSTM, ARIMA and other prediction models.