Open AccessShort-term electric power demand forecastingusing a hybrid model of SARIMA and SVR
Author(s) -
QiheLou,
QiLyu,
Zhixiong Na,
Dayan Ma,
Xiaojun Ma
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/619/1/012035
Subject(s) - computer science , renewable energy , demand response , electric power system , term (time) , smart grid , demand forecasting , time series , support vector machine , power (physics) , econometrics , operations research , electricity , artificial intelligence , economics , engineering , machine learning , physics , quantum mechanics , electrical engineering
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.