Title :
Study of artificial neural network based short term load forecasting
Author :
Webberley, Ashton ; Gao, David Wenzhong
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Denver, Denver, CO, USA
Abstract :
With more and more renewable energy integrated into the power grid and demand response in smart grid environment, electric load forecasting becomes more important. Accurate load forecasting facilitates better renewable energy integration and electricity market operation. Over the years, different load forecasting methods have been developed and applied. Multiple linear regression and artificial neural network based methods are well accepted by industries. This paper focuses on ANN-based method and provides detailed steps of load forecasting including data processing and neural network design.
Keywords :
load forecasting; neural nets; power engineering computing; power markets; regression analysis; smart power grids; artificial neural network; demand response; electric load forecasting; electricity market; multiple linear regression; power grid; renewable energy integration; short term load forecasting; smart grid environment; Artificial neural networks; Biological neural networks; Load forecasting; Load modeling; Mathematical model; Predictive models; Temperature distribution; Load forecast; artificial neural network; electric load forecasting; smart grid;
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
DOI :
10.1109/PESMG.2013.6673036