Smart grid load forecasting using online support vector regression
| Autor: | Arun Kumar Sangaiah, Viera Rozinajová, Anna Bou Ezzeddine, Petra Vrablecová, Slavomír Šárik |
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| Rok vydání: | 2018 |
| Předmět: |
Engineering
General Computer Science business.industry 020209 energy 02 engineering and technology computer.software_genre Grid Ensemble learning Industrial engineering Support vector machine Smart grid Control and Systems Engineering Smart city 0202 electrical engineering electronic engineering information engineering Electricity market Data mining Power engineering Electrical and Electronic Engineering business computer Efficient energy use |
| Zdroj: | Computers & Electrical Engineering. 65:102-117 |
| ISSN: | 0045-7906 |
| DOI: | 10.1016/j.compeleceng.2017.07.006 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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