期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2020
卷号：619
期号：1
DOI：10.1088/1755-1315/619/1/012035
语种：English
出版社：IOP Publishing
摘要：Short-term electric power demand forecasting is the most basic and important application of smart grid. With the rapid development of renewable energy and clean energy in recent years, power demand forecasting gains special attention again. It has a great impact on the planning of power generation units and the purchase and sale of power market. Furthermore, itis also conducive to the realization of demand response and resource allocation efficiency and reliability, thus contributing to the Photovoltaic power generation system as well. Although generous methods have been proposed, it remains to be an open challenge owing to its limited precision. In this paper, a hybrid method of Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) is proposed for hourly forecasting, which overcomes the difficulty of generalization of a single model. The core thought of this model is using SARIMA to fit the linear part of the series and correcting the deviation by SVR. We tested the accuracy of this method on a public data set, and the result shows that it performs much better than the single SARIMA model on the fitting effect. Also, we compared our model with the recent three works, and it demonstrates a decrease of 18.102%,10.534% and 4.757% in forecasting error respectively. To our surprise, when we usedthe previous four hours of data to predict the current data by the single SVR, we got the best performance of all models. In the end, we analyzed the possible causes for this result.
