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Pregled bibliografske jedinice broj: 525017

Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0


Matijaš, Marin; Lipić, Tomislav
Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0 // Proceedings of RapidMiner Community Meeting and Conference
Dortmund, Njemačka, 2010. str. 45-48 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0

Autori
Matijaš, Marin ; Lipić, Tomislav

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of RapidMiner Community Meeting and Conference / - , 2010, 45-48

Skup
RapidMiner Community Meeting and Conference - RCOMM2010

Mjesto i datum
Dortmund, Njemačka, 13 - 16. 9. 2010

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
short-term load forecasting; support vector machine; rapidminer

Sažetak
This paper presents an application of RapidMiner 5.0 in Load Forecasting using Support Vector Machines. In Power Systems, Load Forecasting is an important task since equilibrium of production and consumption in electricity grid must be maintained at all times. Short Term Load Forecasting(STLF) is forecasting of load up to 72 hours in the future. Method of Support Vector Machines (SVM) has been used for Load Forecasting for its good generalization properties which lead to low Mean Absolute Percentage Error (MAPE). Besides its importance in power system planning, differences in MAPE greater than 0, 5% have significant economic effects in liberalized electricity markets for producers and suppliers. The paper presents application of SVM on high-voltage customers’ consumption in Croatian electricity grid in RapidMiner 5.0. SVM is used as a learning algorithm and its parameters are optimized through Grid Parameter Optimization. Mentioned combination of fast Support Vector Machines with good generalization properties and Grid Parameter Optimization, that optimizes a model, is chosen because it provides a solution for STLF with lower MAPE than previously used similar day technique. First section in this paper gives an overview of the field of application. In second section a description of the algorithm is provided. Third section presents the process in RapidMiner which is followed by results and a conclusion.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike i računarstva, Zagreb,
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Tomislav Lipić (autor)

Avatar Url Marin Matijaš (autor)

Citiraj ovu publikaciju

Matijaš, Marin; Lipić, Tomislav
Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0 // Proceedings of RapidMiner Community Meeting and Conference
Dortmund, Njemačka, 2010. str. 45-48 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Matijaš, M. & Lipić, T. (2010) Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0. U: Proceedings of RapidMiner Community Meeting and Conference.
@article{article, year = {2010}, pages = {45-48}, keywords = {short-term load forecasting, support vector machine, rapidminer}, title = {Application of Short Term Load Forecasting using Support Vector Machines in RapidMiner 5.0}, keyword = {short-term load forecasting, support vector machine, rapidminer}, publisherplace = {Dortmund, Njema\v{c}ka} }