关键词: 预测模型, 后向传播算法, 循环神经网络, 时序分解, 电力数据

Abstract: Under the big data background of smart grid popularization, accurate analysis and forecasting of power data is of great guiding significance to the planning of power grids and the management decisions of economic departments. However, most of the models are only studied on a single time scale. Aiming at this problem, a recurrent neural network prediction model based on time series decomposition back propagation algorithm is proposed. Through the statistical analysis of the real consumer electricity consumption data and external factors, the residential electricity consumption characteristics and behavior rules are analyzed in depth. According to the characteristics of the data and external factors such as weather, holidays and other factors affecting the user’s electricity behavior, a forecasting model is established to predict the electricity consumption of users in the future. In addition, taking into account the temporal characteristics of household electricity consumption data showing different variation rules at different time scales, the forecasting model is established by time series decomposition to model the periodicity and trend of user’s electricity consumption behavior and to achieve the goal by weighted fusion training effect, with a certain degree of synergy, improving prediction accuracy.

Key words: prediction model, back propagation algorithm, recurrent neural network, time series decomposition, power data