Power load forecasting research based on neural network and Holt-winters method

Since the electric load shows a very obvious periodicity in time series, increasing the periodic factor in the power load forecasting is a research direction of power load forecasting. It is by adding trend and seasonality (that is, periodicity) to smoothing values that the Holt-winters algorithm improves the accuracy of predictions. In this paper, Holt-winters algorithm and neural network algorithm are used to build power load prediction models respectively, and data of city A in Shandong province is used for testing. Experimental results show that the prediction results of the two algorithms are similar, but the Holt-winters algorithm is slightly more accurate.