Estimating Electricity Consumption Levels in Dwellings Using Artificial Neural NetworksAbstract: Most of the studies on electricity consumption were conducted using econometric models and statistical methods. Studies that aiming at predicting electricity consumption levels using household characteristics and utilizing machine learning methods couldn’t be found in the literature. This study is aiming at presenting a model proposal that predicts the electricity consumption levels in dwellings as lower consumption and higher consumption classes, using household and dwelling characteristics. Artificial Neural Networks were utilized as a machine learning method in modeling phase. Data were gathered from Turkish Statistical Institution’s Household Budget Survey. The records having no electricity consumption were removed and mean electricity consumption was determined from remaining 32,765 households. Records above the mean were labelled as high-consumption class and that are below the mean were labelled as low-consumption class. ANN model training was carried out using 24,574 (70%) household data. Remaining 8,191 (30%) household data were used for testing the model. The success of the model was 75.11% at training phase, and it was 65.56% at testing phase. As a result, the model proposal predicting electricity consumption levels using household and dwelling characteristics to contribute electricity production and distribution planning is presented

Ercan, Uğur; Irmak, Sezgin; Çevi̇k, Kerim Kürşat; Canbazoğlu, Erokan

Estimating Electricity Consumption Levels in Dwellings Using Artificial Neural NetworksAbstract: Most of the studies on electricity consumption were conducted using econometric models and statistical methods. Studies that aiming at predicting electricity consumption levels using household characteristics and utilizing machine learning methods couldn’t be found in the literature. This study is aiming at presenting a model proposal that predicts the electricity consumption levels in dwellings as lower consumption and higher consumption classes, using household and dwelling characteristics. Artificial Neural Networks were utilized as a machine learning method in modeling phase. Data were gathered from Turkish Statistical Institution’s Household Budget Survey. The records having no electricity consumption were removed and mean electricity consumption was determined from remaining 32,765 households. Records above the mean were labelled as high-consumption class and that are below the mean were labelled as low-consumption class. ANN model training was carried out using 24,574 (70%) household data. Remaining 8,191 (30%) household data were used for testing the model. The success of the model was 75.11% at training phase, and it was 65.56% at testing phase. As a result, the model proposal predicting electricity consumption levels using household and dwelling characteristics to contribute electricity production and distribution planning is presented

Uğur Ercan, Sezgin Irmak, Kerim Kürşat Çevi̇k and Erokan Canbazoğlu

Sosyoekonomi Journal, 2020, issue 28(46)

Keywords: Artificial Neural Networks; Electricity Consumption; Classification. (search for similar items in EconPapers)
JEL-codes: C45 C81 D12 (search for similar items in EconPapers)
Date: 2020
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