Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers

With the shift towards a more sustainable energy system, the need for a better understanding of the behavior development over time of consumers and prosumers arises. Despite the growing penetration of smart meter infrastructure, the availability of energy usage behavior data is still limited, due to...

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Autor principal: Werner, Tamo
Otros Autores: Jiao, Jiao
Formato: Tesis de maestría
Lenguaje:Inglés
Publicado: 2024
Materias:
CONSUMO DE ELECTRICIDAD
CONSUMIDORES
APRENDIZAJE AUTOMÁTICO
Acceso en línea:https://ri.itba.edu.ar/handle/20.500.14769/4288
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-4288
record_format dspace
spelling I32-R138-20.500.14769-42882026-01-15T14:39:54Z Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers Werner, Tamo Jiao, Jiao CONSUMO DE ELECTRICIDAD CONSUMIDORES APRENDIZAJE AUTOMÁTICO With the shift towards a more sustainable energy system, the need for a better understanding of the behavior development over time of consumers and prosumers arises. Despite the growing penetration of smart meter infrastructure, the availability of energy usage behavior data is still limited, due to privacy and security concerns. Thus, connecting and comparing existing datasets is the key to observe the user behavior shifts as well as enhancing the utility of the available data. In the present work, a novel work!ow for combined analysis on multiple smart meter datasets is proposed, which links datasets with diferent scopes, temporal origins and speci#cations. In general, there are 4 steps: data preprocessing, clustering, location dependency check and dataset linking. First, the meteorological seasons combined with weekdays and weekends are picked for data segmentation in the data preprocessing, followed by missing value validation and normalization based on the maximum and minimum consumption value of each household. Thereafter, K-means clustering algorithm is applied to group the user behaviors, which stands out by 0.8186 Silhouette coe$cient (SIL) and 0.2884 Davies-Bouldin Index (DBI) among Fuzzy C-Means and hierarchical clustering approach. Subsequently, two validation approaches on the location dependency, cluster center correlation (0.8048) and location share among clusters (4.99 % variability), prove the minor impact of the household location on the electricity consumption behavior within Germany. Based on the location dependency check, ultimately, the combined analysis of the two datasets reveals the temporal development of the residential consumption behaviors. It shows that new technologies, especially Photovoltaics (PV), Electric Vehicles (EV) and heat pumps, have in!uence on the user behavior shift and the energy consumption level. 2024-02-14T16:51:40Z 2024-02-14T16:51:40Z 2021 Tesis de maestría https://ri.itba.edu.ar/handle/20.500.14769/4288 en application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic CONSUMO DE ELECTRICIDAD
CONSUMIDORES
APRENDIZAJE AUTOMÁTICO
spellingShingle CONSUMO DE ELECTRICIDAD
CONSUMIDORES
APRENDIZAJE AUTOMÁTICO
Werner, Tamo
Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
topic_facet CONSUMO DE ELECTRICIDAD
CONSUMIDORES
APRENDIZAJE AUTOMÁTICO
description With the shift towards a more sustainable energy system, the need for a better understanding of the behavior development over time of consumers and prosumers arises. Despite the growing penetration of smart meter infrastructure, the availability of energy usage behavior data is still limited, due to privacy and security concerns. Thus, connecting and comparing existing datasets is the key to observe the user behavior shifts as well as enhancing the utility of the available data. In the present work, a novel work!ow for combined analysis on multiple smart meter datasets is proposed, which links datasets with diferent scopes, temporal origins and speci#cations. In general, there are 4 steps: data preprocessing, clustering, location dependency check and dataset linking. First, the meteorological seasons combined with weekdays and weekends are picked for data segmentation in the data preprocessing, followed by missing value validation and normalization based on the maximum and minimum consumption value of each household. Thereafter, K-means clustering algorithm is applied to group the user behaviors, which stands out by 0.8186 Silhouette coe$cient (SIL) and 0.2884 Davies-Bouldin Index (DBI) among Fuzzy C-Means and hierarchical clustering approach. Subsequently, two validation approaches on the location dependency, cluster center correlation (0.8048) and location share among clusters (4.99 % variability), prove the minor impact of the household location on the electricity consumption behavior within Germany. Based on the location dependency check, ultimately, the combined analysis of the two datasets reveals the temporal development of the residential consumption behaviors. It shows that new technologies, especially Photovoltaics (PV), Electric Vehicles (EV) and heat pumps, have in!uence on the user behavior shift and the energy consumption level.
author2 Jiao, Jiao
author_facet Jiao, Jiao
Werner, Tamo
format Tesis de maestría
author Werner, Tamo
author_sort Werner, Tamo
title Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
title_short Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
title_full Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
title_fullStr Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
title_full_unstemmed Machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
title_sort machine learning-based analysis of residential electricity consumption behavior for consumers and prosumers
publishDate 2024
url https://ri.itba.edu.ar/handle/20.500.14769/4288
work_keys_str_mv AT wernertamo machinelearningbasedanalysisofresidentialelectricityconsumptionbehaviorforconsumersandprosumers
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