Formulation and analysis of a rule-based short-term load forecasting algorithm

The formulation of rules for the rule base and the application of such rules are discussed. The classification of the load forecast parameters into weather-sensitive and nonweather-sensitive categories is described. The rationale underlying the development of rules for both the one-day and seven-day...

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Vydáno v:	Proceedings of the IEEE Ročník 78; číslo 5; s. 805 - 816
Hlavní autor:	Rahman, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.05.1990 Institute of Electrical and Electronics Engineers
Témata:	Algorithm design and analysis Applied sciences Distributed power generation Economic forecasting Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology Load forecasting Operation. Load control. Reliability Power generation Power industry Power networks and lines Power system modeling Predictive models Pricing Weather forecasting Short term Hourly average Error analysis Demand forecasting Expert system Daily variation Monthly variation Load control Electrical network Seasonal variation Daily average Energy management Algorithm analysis
ISSN:	0018-9219
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Abstract	The formulation of rules for the rule base and the application of such rules are discussed. The classification of the load forecast parameters into weather-sensitive and nonweather-sensitive categories is described. The rationale underlying the development of rules for both the one-day and seven-day forecast is presented. This exercise leads to the identification and estimation of parameters relating load, weather variables, day types, and seasons. Sample rules that are the product of identifiable statistical relationships and expert knowledge are examined. A self-learning process is described which shows how rules governing the electric utility load can be updated. Results from both the one-day and seven-day forecast algorithms are presented, where the seven-day forecast is generated using both accurate and predicted weather information. The monthly average load forecast errors range between 2.97% and 10.71% for the seven-day forecasts. For the one-day forecasts, the average seasonal errors range between 1.03% and 1.53%.< >
AbstractList	The formulation and analysis of a short-term load forecasting algorithm is presented. The formulation of rules for the rule base and the application of such rules are discussed. The classification of the load forecast parameters into weather- and non-weather-sensitive categories is discussed. The rationale behind the development of rules for both the one-day and seven-day forecast is then presented. This exercise leads to the identification and estimation of parameters relating load, weather variables, day types, and seasons. The formulation of rules for the rule base and the application of such rules are discussed. The classification of the load forecast parameters into weather-sensitive and nonweather-sensitive categories is described. The rationale underlying the development of rules for both the one-day and seven-day forecast is presented. This exercise leads to the identification and estimation of parameters relating load, weather variables, day types, and seasons. Sample rules that are the product of identifiable statistical relationships and expert knowledge are examined. A self-learning process is described which shows how rules governing the electric utility load can be updated. Results from both the one-day and seven-day forecast algorithms are presented, where the seven-day forecast is generated using both accurate and predicted weather information. The monthly average load forecast errors range between 2.97% and 10.71% for the seven-day forecasts. For the one-day forecasts, the average seasonal errors range between 1.03% and 1.53%.< >
Author	Rahman, S.
Author_xml	– sequence: 1 givenname: S. surname: Rahman fullname: Rahman, S. organization: Dept of Electr. Eng., Virginia Polytech. Inst. & State Univ. Blacksburg, VA, USA
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Cites_doi	10.1109/TPAS.1980.319608 10.1109/59.193840 10.1109/PROC.1987.13927 10.1109/TSMC.1982.4308827 10.1109/59.192889 10.1109/TPAS.1981.316650 10.1057/jors.1982.116 10.1109/59.14540 10.1049/piee.1968.0258 10.1109/59.32476
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Keywords	Short term Hourly average Error analysis Demand forecasting Expert system Daily variation Monthly variation Load control Electrical network Seasonal variation Daily average Energy management Algorithm analysis
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References	ref13 ref12 ref11 bhatnagar (ref4) 1986 ref10 ref2 ref1 fildes (ref9) 1979; 30 ref7 ref3 ref6 ref5 bunn (ref8) 1985
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SubjectTerms	Algorithm design and analysis Applied sciences Distributed power generation Economic forecasting Electrical engineering. Electrical power engineering Electrical power engineering Exact sciences and technology Load forecasting Operation. Load control. Reliability Power generation Power industry Power networks and lines Power system modeling Predictive models Pricing Weather forecasting
Title	Formulation and analysis of a rule-based short-term load forecasting algorithm
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