Improve the unit commitment scheduling by using the neural network based short term load forecasting

被引：0
|
作者：
Saksomchai, T [1 ]
Lee, WJ [1 ]
Methaprayoon, K [1 ]
Liao, J [1 ]
Ross, R [1 ]
机构：
[1] Univ Texas, Energy Syst Res Ctr, Arlington, TX 76109 USA
来源：
2004 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS TECHNICAL CONFERENCE, CONFERENCE RECORD | 2004年
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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：
Unit commitment scheduling of the utility company relies upon the forecast of the demand, demand pattern, availability and capacity of the generators, minimum/maximum up and down time of the generators, and heat rate. According to the experiences of a local utility company, the difference of the fuel cost can reach million dollars per day with different unit commitment scheduling. Accurate hour-ahead and day-ahead demand forecasting play important roles for proper unit commitment scheduling. This paper describes the procedure to improve the unit commitment scheduling by using the hour-ahead and day-ahead results from the newly developed neural network based short-term load forecasting program in the SCADA/EMS system. Comparison of field records is also provided.
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页码：33 / 39
页数：7
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