Short-Term Demand Forecasting By Using ANN Algorithms

被引：0
|
作者：
Singh, Astha [1 ]
Sahay, Kishan Bhushan [1 ]
机构：
[1] Madan Mohan Malaviya Univ Technol, Dept Elect Engn, Gorakhpur, Uttar Pradesh, India
来源：
2018 6TH INTERNATIONAL ELECTRICAL ENGINEERING CONGRESS (IEECON) | 2018年
关键词：
Mean absolute error (MAE); mean absolute percentage error (MAPE); neural network (NN); power system; short-term load forecasting (STLF);
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Electric power demand prediction is an important task for the efficient, economic, reliable and accurate power system operations which can be unit commitment, maintenance scheduling, etc. Considering importance of load forecasting different models are proposed for power demand forecasting. ANN is the most widely used method for power demand forecast from years. Simulation results obtained show that ANN is capable of forecasting electricity loads effectively. In this paper different ANN has been applied in short-term demand forecasting that is, the one hour-ahead hourly forecast of the electricity power demand using MATLAB R14a. The simulation results have shown highly accurate one hour ahead load forecast.
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页数：4
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