Short Term Load Forecasting Using Artificial Neural Network

被引：0

作者：

Singh, Saurabh ^{[1
]}

Hussain, Shoeb ^{[2
]}

Bazaz, Mohammad Abid ^{[1
]}

机构：

[1] Natl Inst Technol, Dept Elect Engn, Srinagar, Jammu & Kashmir, India

[2] Univ Kashmir, Dept Elect Engn, Srinagar, Jammu & Kashmir, India

来源：

2017 FOURTH INTERNATIONAL CONFERENCE ON IMAGE INFORMATION PROCESSING (ICIIP) | 2017年

关键词：

Artificial neural network (ANN); Artificial Intelligence (AI); Short term load forecasting (STLF); Mean absolute percent error (MAPE); Mean absolute error (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：

Short term load forecasting is required for power system planning, operation and control. It is used by utilities, system operators, generators, power marketers. In this paper, load forecasting has been done using ANN (Artificial Neural Network). As load profile is different for weekdays and weekends, so for better forecasting performance, training of neural network has been done separately for weekdays and weekends. Accordingly forecasting is done separately for weekdays and weekends. Neural network toolbox with 20 neurons has been used for forecasting load of NEPOOL region of ISO New England. Hourly temperature (Dry bulb), humidity (Dew point) sand electricity load of NEPOOL region has been taken from 2004 to 2008. ANN model is trained on hourly data from 2004 to 2007 and tested on out-of-sample data from 2008. The test set is used only for forecasting to test the performance of the model on out-of-sample data. Simulation results obtained have shown the comparison of actual and forecasted load data. Performance of forecaster is calculated using MAE, MAPE and daily peak forecast error.

引用

页码：159 / 163

页数：5

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