Short-Term Load Forecasting of Toronto Canada by Using Different ANN Algorithms

被引：0
|
作者：
Sahay, Kishan Bhushan [1 ]
Sahu, Suneet [1 ]
Singh, Pragya [1 ]
机构：
[1] Madan Mohan Malaviya Univ Technol, Dept Elect Engn, Gorakhpur, Uttar Pradesh, India
来源：
2016 IEEE 6TH INTERNATIONAL CONFERENCE ON POWER SYSTEMS (ICPS) | 2016年
关键词：
Mean absolute error (MAE); mean absolute percentage error (MAPE); neural network (NN); power system; short-term load forecasting;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In the field of power system, electrical load forecasting has got wide acceptance due to the determination of future trends of demand. Electrical load forecasting is used to predict the future power demand of the consumers which is estimated on the basis of the historical load data. The forecast model processes the exogenous relation of the provided data and consequently anticipates the future load demand. In this paper different ANN algorithms i.e Levenberg Marquardt back propagation, Bayesian Regularization & Scaled Conjugate Gradient algorithms has been applied for short-term load forecasting i.e. the one hour-ahead hourly forecast of the electricity load of Toronto, Canada using MATLAB R14a. The data used in the forecasting are hourly historical data of the temperature & the electricity demand of Toronto, Canada. The simulation results obtained is highly accurate and well forecasted.
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页数：6
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