DocumentCode :
2089090
Title :
Forecasting peak loads with neural networks
Author :
Garcia, Luis F. ; Mohammed, Osama A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA
fYear :
1994
fDate :
10-13 Apr 1994
Firstpage :
351
Lastpage :
356
Abstract :
This paper presents a new approach to power load forecasting using artificial neural networks (ANN). Based on weather conditions and past history of load consumption, a load forecast is made by the utility companies to deliver the appropriate load to its customers. Power systems operation and planning functions such as unit commitment, security analysis, state estimation, etc. are benefited with an accurate load forecast. Improving the accuracy of the load forecast can save a significant amount of money. Artificial neural networks permit adaptability to climate changes compared to other forecasting methods in use. The results obtained by using ANN have been found to give better results than other conventional techniques
Keywords :
load forecasting; neural nets; power system analysis computing; artificial neural networks; climate changes; load consumption; peak loads forecasting; power load forecasting; power systems operation; power systems planning; security analysis; state estimation; unit commitment; weather conditions; Artificial neural networks; Character generation; Load forecasting; Neural networks; Power engineering and energy; Power engineering computing; Power system planning; Power system security; Test pattern generators; Weather forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Southeastcon '94. Creative Technology Transfer - A Global Affair., Proceedings of the 1994 IEEE
Conference_Location :
Miami, FL
Print_ISBN :
0-7803-1797-1
Type :
conf
DOI :
10.1109/SECON.1994.324334
Filename :
324334
Link To Document :
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