Research on short-term power load forecasting based on LSTM

Abstract

In order to improve the short-term power load prediction results, this paper uses long short-term memory network to predict short-term power load. With the improvement of accuracy requirements, the traditional load forecasting does not consider problems such as time series and eigenvalues. This paper proposes a long short-term memory network model to predict load. First, the data set is processed, cleaned and normalized, and the data set is divided into training set and sample set. Then, a prediction model is built, and appropriate parameters and eigenvalues are selected for the model to study the impact on the short-term power load under the LSTM model. This paper uses the Short-term electricity load forecasting (Panama) dataset on the kaggle platform to verify the model, and uses multivariate and multistep to forecast short-term electricity load.