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ELECTRICITY DEMAND PREDICTION OF LARGE COMMERCIAL BUILDINGS USING SUPPORT VECTOR MACHINE

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dc.contributor.advisor Hemapala, KTMU
dc.contributor.advisor Jayasekara, B
dc.contributor.author Samarawickrama, NGIS
dc.date.accessioned 2015-11-27T11:12:45Z
dc.date.available 2015-11-27T11:12:45Z
dc.date.issued 2015-11-27
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/11518
dc.description.abstract In an ideal competitive commercial world, having accurate energy forecasting tool becomes a Key Performance Indicator (KPI) for the building owners. Energy forecasting plays a crucial role for any building when it undergoes the retrofitting works in order to maximize the benefits and utilities. This thesis elaborates accurate energy forecasting tools based on Support Vector Machine Regression (SVMR). SVM is one of the most important methods which is widely applied in different literatures, in forecasting and regression of random data sets. It estimates the regression using kernel function, which is composed of a set of linear functions that is defined in a high-dimensional feature space while, inputs having nonlinear performance. In the case study, four commercial buildings in Colombo, Sri Lanka, are randomly selected and the models were developed and tested using monthly landlord utility bills. Careful analysis of data identified three important parameters, (Dry-bulb temperature (T), Solar Radiation (SR) and Relative humidity (RH)), which have significant contribution to the model, which is under consideration. Stepwise searching method is used to investigate the performance of SVM with respect to the three tunable parameters, C, γ and ε; and thereby to develop the radial-basis function (RBF) kernel. The results showed that the structure of the training set has significant effect to the accuracy of the prediction. The analysis of the experimental results reveal that all the forecasting models give an acceptable result for all four commercials buildings with low coefficient of variance & a low percentage error (% error). en_US
dc.language.iso en en_US
dc.title ELECTRICITY DEMAND PREDICTION OF LARGE COMMERCIAL BUILDINGS USING SUPPORT VECTOR MACHINE en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree Master of Science in industrial automation en_US
dc.identifier.department Department of Electrical Engineering en_US
dc.date.accept 2014-07
dc.identifier.accno 108928 en_US


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