Title :
Short Term Power Load Forecasting by Using Neural Models
Author :
Chen, Huang-Chi ; Lin, Yi-Ching ; Chen, Yu-Ju ; Chang, Chuo-Yean ; Huang, Huang-Chu ; Hwang, Rey-Chue
Author_Institution :
Electr. Eng. Dept., I-Shou Univ., Kaohsiung, Taiwan
Abstract :
This paper presents the power load forecasting by using neural models for Toronto area, Canada. Different neural models were used to carry out the forecasting works. One-day-ahead daily total load and peak load forecasts were implemented by using different neural models in order to find the more accurate forecasting results. The load data and temperatures provided by Independent Electricity System Operator (IESO) from January, year 2003 to January, year 2008 were studied and simulated. In our studies, mean absolute percentage error (MAPE) is used as the measurement of model´s performances.
Keywords :
feedforward neural nets; load forecasting; power engineering computing; Canada; IESO; Independent Electricity System Operator; feedforward fully connected neural network; mean absolute percentage error; neural models; short term power load forecasting; Economic forecasting; Load forecasting; Neural networks; Power system modeling; Power system planning; Power system security; Power system simulation; Predictive models; Signal processing; Weather forecasting;
Conference_Titel :
Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4244-5543-0
DOI :
10.1109/ICICIC.2009.330
