Research Article
Trends in Short-Term Renewable and Load Forecasting for Applications in Smart Grid
@INPROCEEDINGS{10.1007/978-3-319-33681-7_24, author={Dongchan Lee and Jangwon Park and Deepa Kundur}, title={Trends in Short-Term Renewable and Load Forecasting for Applications in Smart Grid}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={Load forecasting Renewable forecasting Demand response Microgrid Energy management system Neural network Support vector machine Smart grid}, doi={10.1007/978-3-319-33681-7_24} }- Dongchan Lee
Jangwon Park
Deepa Kundur
Year: 2016
Trends in Short-Term Renewable and Load Forecasting for Applications in Smart Grid
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_24
Abstract
The development of smart grid paradigm enabled greater integration of renewable energy sources into the generation mix based on the renewable and load forecasting. This paper presents a review of applications and recent development in short-term forecasting methods for smart grids. We look at the characteristics and limitations of the methods and how they are used to improve the performance of smart grids. While the existing forecasting methods such as time series models and artificial intelligence have been successful, we focus on the new applications that rise in smart electric grid. There is an increasing interest in using distributed generation such as in microgrids, and as a result, the demand for forecasting at distribution system level is growing.
