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A platform for research: civil engineering, architecture and urbanism
Development of electricity consumption profiles of residential buildings based on smart meter data clustering
https://orcid.org/0000-0002-7572-6418
https://orcid.org/0000-0001-9656-0173
https://orcid.org/0000-0001-6537-7982
https://orcid.org/0000-0002-2320-3666
https://orcid.org/0000-0003-0327-0316
Graphical abstract Display Omitted
Abstract In the present research, a high-resolution, detailed electric load dataset was assessed, collected by smart meters from nearly a thousand households in Hungary, many of them single-family houses. The objective was to evaluate this database in detail to determine energy consumption profiles from time series of daily and annual electric load. After representativity check of dataset daily and annual energy consumption profiles were developed, applying three different clustering methods (k-means, fuzzy k-means, agglomerative hierarchical) and three different cluster validity indexes (elbow method, silhouette method, Dunn index) in MATLAB environment. The best clustering method for our examination proved to be the k-means clustering technique. Analyses were carried out to identify different consumer groups, as well as to clarify the impact of specific parameters such as meter type in the housing unit (e.g. peak, off-peak meter), day of the week (e.g. weekend, weekday), seasonality, geographical location, settlement type and housing type (single-family house, flat, age class of the building). Furthermore, four electric user profile types were proposed, which can be used for building energy demand simulation, summer heat load and winter heating demand calculation.
Development of electricity consumption profiles of residential buildings based on smart meter data clustering
https://orcid.org/0000-0002-7572-6418
https://orcid.org/0000-0001-9656-0173
https://orcid.org/0000-0001-6537-7982
https://orcid.org/0000-0002-2320-3666
https://orcid.org/0000-0003-0327-0316
Graphical abstract Display Omitted
Abstract In the present research, a high-resolution, detailed electric load dataset was assessed, collected by smart meters from nearly a thousand households in Hungary, many of them single-family houses. The objective was to evaluate this database in detail to determine energy consumption profiles from time series of daily and annual electric load. After representativity check of dataset daily and annual energy consumption profiles were developed, applying three different clustering methods (k-means, fuzzy k-means, agglomerative hierarchical) and three different cluster validity indexes (elbow method, silhouette method, Dunn index) in MATLAB environment. The best clustering method for our examination proved to be the k-means clustering technique. Analyses were carried out to identify different consumer groups, as well as to clarify the impact of specific parameters such as meter type in the housing unit (e.g. peak, off-peak meter), day of the week (e.g. weekend, weekday), seasonality, geographical location, settlement type and housing type (single-family house, flat, age class of the building). Furthermore, four electric user profile types were proposed, which can be used for building energy demand simulation, summer heat load and winter heating demand calculation.
Development of electricity consumption profiles of residential buildings based on smart meter data clustering
https://orcid.org/0000-0002-7572-6418
https://orcid.org/0000-0001-9656-0173
https://orcid.org/0000-0001-6537-7982
https://orcid.org/0000-0002-2320-3666
https://orcid.org/0000-0003-0327-0316
Graphical abstract Display Omitted
Abstract In the present research, a high-resolution, detailed electric load dataset was assessed, collected by smart meters from nearly a thousand households in Hungary, many of them single-family houses. The objective was to evaluate this database in detail to determine energy consumption profiles from time series of daily and annual electric load. After representativity check of dataset daily and annual energy consumption profiles were developed, applying three different clustering methods (k-means, fuzzy k-means, agglomerative hierarchical) and three different cluster validity indexes (elbow method, silhouette method, Dunn index) in MATLAB environment. The best clustering method for our examination proved to be the k-means clustering technique. Analyses were carried out to identify different consumer groups, as well as to clarify the impact of specific parameters such as meter type in the housing unit (e.g. peak, off-peak meter), day of the week (e.g. weekend, weekday), seasonality, geographical location, settlement type and housing type (single-family house, flat, age class of the building). Furthermore, four electric user profile types were proposed, which can be used for building energy demand simulation, summer heat load and winter heating demand calculation.
Development of electricity consumption profiles of residential buildings based on smart meter data clustering
Czétány, László (author) / Vámos, Viktória (author) / Horváth, Miklós (author) / Szalay, Zsuzsa (author) / Mota-Babiloni, Adrián (author) / Deme-Bélafi, Zsófia (author) / Csoknyai, Tamás (author)
Energy and Buildings ; 252
2021-08-18
Article (Journal)
Electronic Resource
English
| Elsevier | 2025
Data-driven load profiles and the dynamics of residential electricity consumption
| BASE | 2022