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Some methods for reducing the total consumption and production prediction errors of electricity: Adaptive Linear Regression of Original Predictions and Modeling of Prediction Errors
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för matematik (MA).
2014 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
##### Abstract [en]

Balance between energy consumption and production of electricityis a very important for the electric power system operation and planning. Itprovides a good principle of effective operation, reduces the generation costin a power system and saves money. Two novel approaches to reduce thetotal errors between forecast and real electricity consumption wereproposed. An Adaptive Linear Regression of Original Predictions (ALROP)was constructed to modify the existing predictions by using simple linearregression with estimation by the Ordinary Least Square (OLS) method.The Weighted Least Square (WLS) method was also used as an alternativeto OLS. The Modeling of Prediction Errors (MPE) was constructed in orderto predict errors for the existing predictions by using the Autoregression(AR) and the Autoregressive-Moving-Average (ARMA) models. For thefirst approach it is observed that the last reported value is of mainimportance. An attempt was made to improve the performance and to getbetter parameter estimates. The separation of concerns and the combinationof concerns were suggested in order to extend the constructed approachesand raise the efficacy of them. Both methods were tested on data for thefourth region of Sweden (“elområde 4”) provided by Bixia. The obtainedresults indicate that all suggested approaches reduce the total percentageerrors of prediction consumption approximately by one half. Resultsindicate that use of the ARMA model slightly better reduces the total errorsthan the other suggested approaches. The most effective way to reduce thetotal consumption prediction errors seems to be obtained by reducing thetotal errors for each subregion.

2014. , s. 65
##### Nyckelord [en]
Adaptive Linear Regression of Original Predictions, Modeling of Prediction Errors, Total Consumption Prediction Errors of Electricity, ARMA, OLS, WLS, TECP, TECC, TPE, MAPE, MPE
Matematik
##### Identifikatorer
OAI: oai:DiVA.org:lnu-34398DiVA, id: diva2:719691
##### Utbildningsprogram
Matematik och modellering, masterprogram, 120 hp
##### Presentation
2014-05-14, Växjö, 11:15 (Engelska)

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Some methods for reducing the total consumption and production prediction errors of electricity(1521 kB)213 nedladdningar
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Filnamn FULLTEXT01.pdfFilstorlek 1521 kBChecksumma SHA-512
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##### Av organisationen
Institutionen för matematik (MA)
Matematik

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