Short Term Load Forecasting of Indian System Using Linear Regression and Artificial Neural Network

被引：0
|
作者：
Patel, Harsh [1 ]
Pandya, Mahesh [1 ]
Aware, Mohan [2 ]
机构：
[1] Lukhadhirj Engn Coll, Dept Elect Engn, Morbi, India
[2] Visvesvaraya Natl Inst Technol, Dept Elect Engn, Nagpur, Maharashtra, India
来源：
2015 5TH NIRMA UNIVERSITY INTERNATIONAL CONFERENCE ON ENGINEERING (NUICONE) | 2015年
关键词：
Short Term Load Forecasting (STLF); Linear Regression (LR); Artificial Neural Network (ANN); Levenberg-Marquardt Back Propagation (LMBP); MAPE; MAE;
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：
The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to study the STLF. In the ANN feed forward network is used for the hourly load forecasting. One fast training algorithm the Levenberg-Marquardt Back Propagation (LMBP) is used to train the neural network. The neuron model is trained using the historical load data of Indian distribution system. The sensitivity of the weather data for the STLF is verified. Both the techniques the LR and the ANN are compared according to the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE). The accuracy of the ANN technique for the STLF with the weather data is proved for the residential and the industrial feeder.
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页数：5
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