Abstract:

In view of the problem that the existing power load forecasting models rely on recent data, which leads to the prediction results deviating from the real situation of the time series, a power load forecasting model based on extended period information is proposed. The pre-processed power load time series is processed according to the same time and different days. On this basis, the ARIMA(Autoregressive Integrated Maving Average Model) model and LSTM(Long Short-Term Memory Network) model are used for modeling and analysis, and three evaluation indicators are used to evaluate the predictive performance of the model. The prediction results show that the three evaluation indexes of the ARIMA model constructed by expanding the period information are lower than those of the traditional ARIMA model, and the corresponding RMSE(Root Mean Square Error), MAE(Mean Absolute Error) and MAPE(Mean Absolute Percentage Error) are 32 434.114 8, 5 828.390 9 and 0.025 2, respectively. The LSTM model of expanding the period information is also lower than the original LSTM model, and the corresponding RMSE, MAE, and MAPE are 13 520.497 4, 9 298.352 6, and 0.091 4,respectively.

Key words: power system, load forecasting, ARIMA, LSTM, expansion cycle, time series, short and medium term forecasts, evaluation indicator