Prediction of daily maximum temperature using a support vector regression algorithm

Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, sinc...

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Vydané v:Renewable energy Ročník 36; číslo 11; s. 3054 - 3060
Hlavní autori: Paniagua-Tineo, A., Salcedo-Sanz, S., Casanova-Mateo, C., Ortiz-García, E.G., Cony, M.A., Hernández-Martín, E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.11.2011
Elsevier
Predmet:
air conditioning
Algorithms
Applied sciences
atmospheric pressure
Daily maximum temperature prediction
electricity
Energy
Europe
Exact sciences and technology
heat
Heating
Mathematical analysis
Neural networks
prediction
Regression
Relative humidity
Stations
Support vector regression algorithms
temperature
Vectors (mathematics)
Europe
Support vector regression algorithms
Daily maximum temperature prediction
Neural networks
ISSN:0960-1481, 1879-0682
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Shrnutí:Daily maximum temperature can be used a good indicator of peak energy consumption, since it can be used to predict the massive use of heating or air conditioning systems. Thus, the prediction of daily maximum temperature is an important problem with interesting applications in the energy field, since it has been proven that electricity demand depends much on weather conditions. This paper presents a novel methodology for daily maximum temperature prediction, based on a Support Vector Regression approach. The paper is focused on different measuring stations in Europe, from which different meteorological variables have been obtained, including temperature, precipitation, relative humidity and air pressure. Two more variables are also included, specifically synoptic situation of the day and monthly cycle. Using this pool of prediction variables, it is shown that the SVMr algorithm is able to give an accurate prediction of the maximum temperature 24 h later. In the paper SVMr technique applied is fully described, including some bounds on the machine hyper-parameters in order to speed up the SVMr training process. The performance of the SVMr has been compared to that of different neural networks in the literature: a Multi-layer perceptron and an Extreme Learning Machine. ► This paper presents a SVMr approach to daily temperature prediction. ► Different measuring stations in Europe, with different meteorological variables are considered. ► Comparisons with alternative neural methods have shown the good performance of the proposed approach.
Bibliografia:http://dx.doi.org/10.1016/j.renene.2011.03.030
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ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2011.03.030