Forecasting Model of Electricity Sales Market Indicators With Distributed New Energy Access

Abstract

It is difficult for the existing electricity sales market to adapt to the vast amount of distributed new energy access. This article proposes an electricity sales market index prediction model for high proportion distributed new energy access under the cloud-side cooperation architecture. First, an index prediction system is designed based on the cloud edge collaboration architecture. The edge computing center processes regional data nearby to improve prediction efficiency. Second, on the edge side, a K-means clustering algorithm is used to classify the data. Third, the power data, distributed power output data, load data, weather data, holiday information, and electricity price data are obtained. Finally, the ConvLSTM-Adaboost prediction model is built in the cloud center. The ConvLSTM is used as the base learner, and the Adaboost-integrated algorithm is used for serial training. At the same time, the prediction results of each base learner are weighted and integrated to obtain the final power and load prediction results of the electricity sales market. Experiments show that the prediction results of MAE, PMSE, and MAPE of the proposed model for daily electricity are 52.539MW, 56.859MW, and 2.063%, respectively. Not only is this superior to other models, but it provides a better analysis of influencing factors.