Title :
Artificial neural network based hourly load forecasting for decentralized load management
Author :
Mandal, J.K. ; Sinha, A.K.
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., Kharagpur, India
Abstract :
Decentralised load management is an essential part of the power system operation. Forecasting load demand at the substation level is generally more difficult and less accurate compared to forecasting total system load demand. In this paper, multi-layered feedforward (MLFF) neural network is used to predict the bus-load demand at the substation level. The MLFF network is trained using the backpropagation (BP) algorithm with an adaptive learning technique. The algorithm is tested for two systems having different load patterns
Keywords :
backpropagation; feedforward neural nets; load forecasting; load management; multilayer perceptrons; power system analysis computing; substations; adaptive learning technique; backpropagation algorithm; bus-load demand prediction; decentralized load management; hourly load forecasting; load demand forecasting; multi-layered feedforward neural network; power system operation; substation; Artificial neural networks; Backpropagation algorithms; Demand forecasting; Feedforward neural networks; Load forecasting; Load management; Multi-layer neural network; Neural networks; Power system management; Substations;
Conference_Titel :
Energy Management and Power Delivery, 1995. Proceedings of EMPD '95., 1995 International Conference on
Print_ISBN :
0-7803-2981-3
DOI :
10.1109/EMPD.1995.500701
