Short-term power load forecasting based on similar day selection and improved LSTM

The electric power industry is the most important basic energy industry in the development of the national economy. The operation control and dispatch of the electric power system is of great significance in ensuring the planning of the electric power system, industrial development, economic operation and environmental protection. Short-term power load forecasting plays an important role in power system operation control and dispatch. This paper proposes a combined power load forecasting model based on similar day selection and improved LSTM. First, a combined similar day screening model based on grey relational analysis and cosine similarity is constructed, which makes up for the shortcomings of the single selection method. The training set and test set ensure the quality of the input data of the model; then use the single-feature time series training to increase the dropout layer of the LSTM model for power load prediction, reduce the resource consumption of training and prediction, and effectively alleviate the occurrence of overfitting., to improve the robustness of the model. The prediction results of the two examples basically coincide with the real value in trend, which confirms that the similar daily screening and improved LSTM combined prediction model constructed in this paper can provide reliable support for power load forecasting and other time-series data forecasting applications.