Title :
Artificial neural network for load forecasting in smart grid
Author :
Zhang, Hao-tian ; Xu, Fang-yuan ; Zhou, Long
Author_Institution :
Energy Syst. Group, City Univ. London, London, UK
Abstract :
It is an irresistible trend of the electric power improvement for developing the smart grid, which applies a large amount of new technologies in power generation, transmission, distribution and utilization to achieve optimization of the power configuration and energy saving. As one of the key links to make a grid smarter, load forecast plays a significant role in planning and operation in power system. Many ways such as Expert Systems, Grey System Theory, and Artificial Neural Network (ANN) and so on are employed into load forecast to do the simulation. This paper intends to illustrate the representation of the ANN applied in load forecast based on practical situation in Ontario Province, Canada.
Keywords :
artificial intelligence; load forecasting; neural nets; power engineering computing; power system planning; smart power grids; ANN; Canada; artificial neural network; load forecasting; planning; power system; smart grid; Artificial neural networks; Load forecasting; Load modeling; Meteorology; Neurons; Simulation; Training; Artificial Neuron Network; Load forecast; Matlab; back propagation training;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580713