Smart grid load forecasting using online support vector regression. (January 2018)

• Record Type: Journal Article
• Authors: Vrablecová, Petra; Bou Ezzeddine, Anna; Rozinajová, Viera; Šárik, Slavomír; Sangaiah, Arun Kumar
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 102
• Page End: 117
• Publication Date: 2018-01
• Subjects: Smart grid -- Short term load forecasting -- Support vector regression -- Online processing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.07.006
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Smart grid, an integral part of a smart city, provides new opportunities for efficient energy management, possibly leading to big cost savings and a great contribution to the environment. Grid innovations and liberalization of the electricity market have significantly changed the character of data analysis in power engineering. Online processing of large amounts of data continuously generated by the smart grid can deliver timely and precise power load forecasts – an important input for interactions on the market where the energy can be contracted even minutes ahead of its consumption to minimize the grid imbalances. We demonstrate the suitability of online support vector regression (SVR) method to short term power load forecasting and thoroughly explore its pros and cons. We present a comparison of ten state-of-the-art forecasting methods in terms of accuracy on public Irish CER dataset. Online SVR achieved accuracy of complex tree-based ensemble methods and advanced online methods.