Economic variables and electricity consumption in Northern Cyprus

Egelioğlu, Fuat; Mohamad, Azah; Güven, H

doi:10.1016/s0360-5442(01)00008-1

Cited by 103 publications

(47 citation statements)

References 8 publications

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“…And, taking the results of system (10) into system (4), we can estimate the parameters (11) to make an electricity consumption forecasting study. We also apply the GM (1, 1) and ANN models for comparison purposes.…”

Section: Case Studymentioning

confidence: 99%

“…A multiple regression model was developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. Egelioglu et al [11] studied the influence of economic variables on the annual electricity consumption in northern Cyprus between 1988 and 1997. Through multiple regression analysis, it was found that the number of customers, the price of electricity, and the number of tourists correlated with the annual electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Modeling and Simulation Analysis of a Power System in China, Based on a Class of Semi-Parametric Regression Approach

Wang

Chen

Yang

2012

sajie

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Forecasting electricity consumption is one of the most important challenges in electricity system planning. This paper presents an improved semi-parametric regression model using the Student distribution function of residual to replace the nonparametric component of the traditional semi-parametric model, thus eliminating the effects of the residual disturbance term. Compared with general linear models, the models make statistical inferences and can automatically regulate the boundary effect, which gives the forecast result a higher accuracy. A case study using data from China is presented to demonstrate the effectiveness of the approach. OPSOMMINGDie vooruitskatting van elektrisiteitverbruik is een van die belangrikste uitdagings in elektrisiteitstelselbeplanning. Dié artikel bevat 'n verbeterde, semi-parametriese regressiemodel, wat gebruik maak van die Studentverdelingsfunksie van residuee om die nie-parametriese komponent van die tradisionele semi-parametriese model te vervang, en sodoende die effekte van die residuversteuringsterm uit te skakel. In vergelyking met algemene lineêre modelle, kan die model statistiese afleidings maak en outomaties die grenseffek reguleer, wat lei tot groter akuraatheid van die vooruitskatting. 'n Gevallestudie wat gebruik maak van data van China demonstreer die effektiwiteit van die benadering.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Modeling and Simulation Analysis of a Power System in China, Based on a Class of Semi-Parametric Regression Approach

Wang

Chen

Yang

2012

sajie

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“…The regression models have been widely used in predicting heat load, cooling load and energy consumption of a building, are successful in prediction, since the values obtained by the models are well matched with the measured data and/or dynamic building energy simulation models [4][5][6][7][8][9][10]. Hygh et al [9] implemented multi-variated linear regression model for an existing building based on 27 building parameters including orientation, aspect ratio, window to wall ratio and shading projection.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

Turhan

Kazanasmaz

Uygun

et al. 2014

Energy and Buildings

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a b s t r a c tThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.

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“…A number of electricity forecasting models have been developed using economic, social, geographic and demographic factors [1][2][3][4][5][6][7]. Egelioglu et al [1] studied the influence of economic variables on the annual electricity consumption in Northern Cyprus by using multiple linear regression analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Egelioglu et al [1] studied the influence of economic variables on the annual electricity consumption in Northern Cyprus by using multiple linear regression analysis. It was found that the number of customers, the price of electricity and the number of tourists correlate with annual electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

06/00151 Forecasting electricity consumption in NewZealand using economic and demographic variables

2006

Fuel and Energy Abstracts

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The influence of selected economic and demographic variables on the annual electricity consumption in New Zealand has been investigated. The study uses gross domestic product, average price of electricity and population of New Zealand during the period 1965-1999. Models are developed using multiple linear regression analysis. It was found that the electricity consumption correlated effectively with all variables. Forecasts made using these models were compared with some available national forecasts. The forecasts are also compared with the forecasts of the previously developed Logistic model. q

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Economic variables and electricity consumption in Northern Cyprus

Cited by 103 publications

References 8 publications

Forecasting Modeling and Simulation Analysis of a Power System in China, Based on a Class of Semi-Parametric Regression Approach

Forecasting Modeling and Simulation Analysis of a Power System in China, Based on a Class of Semi-Parametric Regression Approach

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

06/00151 Forecasting electricity consumption in NewZealand using economic and demographic variables

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