A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Electricity consumption analysis based on Turkish Household Budget Surveys
https://orcid.org/http://orcid.org/0000-0001-9828-7385
https://orcid.org/http://orcid.org/0000-0001-7093-670X
https://orcid.org/http://orcid.org/0000-0003-1605-7661
https://orcid.org/http://orcid.org/0000-0001-8548-6377
https://orcid.org/http://orcid.org/0000-0003-2319-6491
This study was designed to analyse the interaction of household surveys and electricity consumption in Turkey. We used the micro-data set of the Household Budget Survey published by TurkStat for the period 2002–2017. A statistical method and an optimisation method, namely Principal Component Analysis (PCA) and Analytical Hierarchical Processing (AHP), were used to explore the correlations between the components of the household surveys. The first 35 variables among 50 variables were extracted using PCA, and expert opinions validated 26 of 35 ranked using the AHP method. The Artificial Neural Networks (ANN) model, constructed using the input variables defined by expert opinions, gave better prediction results than the ANN model defined using the PCA outcome. ANN sensitivity analysis was conducted to examine the prediction for the components that were validated by AHP evaluations. The results show that dwelling characteristics had more impact on electricity utilisation than did ownership of appliances. It was also discovered that the amount of total expenditure had a negligible impact on electricity consumption.
Electricity consumption analysis based on Turkish Household Budget Surveys
https://orcid.org/http://orcid.org/0000-0001-9828-7385
https://orcid.org/http://orcid.org/0000-0001-7093-670X
https://orcid.org/http://orcid.org/0000-0003-1605-7661
https://orcid.org/http://orcid.org/0000-0001-8548-6377
https://orcid.org/http://orcid.org/0000-0003-2319-6491
This study was designed to analyse the interaction of household surveys and electricity consumption in Turkey. We used the micro-data set of the Household Budget Survey published by TurkStat for the period 2002–2017. A statistical method and an optimisation method, namely Principal Component Analysis (PCA) and Analytical Hierarchical Processing (AHP), were used to explore the correlations between the components of the household surveys. The first 35 variables among 50 variables were extracted using PCA, and expert opinions validated 26 of 35 ranked using the AHP method. The Artificial Neural Networks (ANN) model, constructed using the input variables defined by expert opinions, gave better prediction results than the ANN model defined using the PCA outcome. ANN sensitivity analysis was conducted to examine the prediction for the components that were validated by AHP evaluations. The results show that dwelling characteristics had more impact on electricity utilisation than did ownership of appliances. It was also discovered that the amount of total expenditure had a negligible impact on electricity consumption.
Electricity consumption analysis based on Turkish Household Budget Surveys
https://orcid.org/http://orcid.org/0000-0001-9828-7385
https://orcid.org/http://orcid.org/0000-0001-7093-670X
https://orcid.org/http://orcid.org/0000-0003-1605-7661
https://orcid.org/http://orcid.org/0000-0001-8548-6377
https://orcid.org/http://orcid.org/0000-0003-2319-6491
This study was designed to analyse the interaction of household surveys and electricity consumption in Turkey. We used the micro-data set of the Household Budget Survey published by TurkStat for the period 2002–2017. A statistical method and an optimisation method, namely Principal Component Analysis (PCA) and Analytical Hierarchical Processing (AHP), were used to explore the correlations between the components of the household surveys. The first 35 variables among 50 variables were extracted using PCA, and expert opinions validated 26 of 35 ranked using the AHP method. The Artificial Neural Networks (ANN) model, constructed using the input variables defined by expert opinions, gave better prediction results than the ANN model defined using the PCA outcome. ANN sensitivity analysis was conducted to examine the prediction for the components that were validated by AHP evaluations. The results show that dwelling characteristics had more impact on electricity utilisation than did ownership of appliances. It was also discovered that the amount of total expenditure had a negligible impact on electricity consumption.
Electricity consumption analysis based on Turkish Household Budget Surveys
Energ. Ecol. Environ.
Kayalica, M. Ozgur (author) / Ozozen, Avni (author) / Guven, Denizhan (author) / Kayakutlu, Gulgun (author) / Bayar, Ayse Aylin (author)
Energy, Ecology and Environment ; 5 ; 444-455
2020-12-01
12 pages
Article (Journal)
Electronic Resource
English
Characterization of Household Electricity Consumption in Uruguay
| TIBKAT | 2024
District household electricity consumption pattern analysis based on auto-encoder algorithm
| BASE | 2019