Title :
Forecasting Power Demand Using Artificial Neural Networks For Sri Lankan Electricity Power System
Author :
Madhugeeth, K.P.M. ; Premaratna, H.L.
Author_Institution :
Sch. of Comput., Univ. of Colombo, Colombo
Abstract :
Accurate models for electricity power load forecasting are essential to the operation and planning of an electricity company. Neural Networks are considered as a computational model that is capable of doing nonlinear curve fitting. In this research, the application of neural networks to study the design of Short Term load Forecasting (STLF) Systems for Sri Lanka was explored. Three layered neural network architecture with back propagation algorithm is proposed to model STLF. The results show that neural network gives the minimum forecasting error compared to the statistical forecasting models and hence it can be considered as an effective method to model the STLF systems for Sri Lankan electricity power system.
Keywords :
artificial intelligence; load forecasting; neural nets; power system analysis computing; power system planning; Sri Lankan electricity power system; artificial neural networks; back propagation algorithm; electricity company operation; electricity company planning; electricity power load forecasting; minimum forecasting error; nonlinear curve fitting; power demand forecasting; short term load forecasting systems; statistical forecasting models; Artificial neural networks; Computational modeling; Computer networks; Load forecasting; Neural networks; Power demand; Power system modeling; Power system planning; Power systems; Predictive models; Back propagation; Non linear curve fitting; Short-term load forecasting; Statistical forecasting models;
Conference_Titel :
Industrial and Information Systems, 2008. ICIIS 2008. IEEE Region 10 and the Third international Conference on
Conference_Location :
Kharagpur
Print_ISBN :
978-1-4244-2806-9
Electronic_ISBN :
978-1-4244-2806-9
DOI :
10.1109/ICIINFS.2008.4798394
