Forecasting household energy consumption based on lifestyle data using hybrid machine learning
Abstract Household lifestyle play a significant role in appliance consumption. The overall effects are that, it can be a determining factor in the healthy functioning of the household appliance or its abnormal functioning. The rapid growth in residential consumption has raised serious concerns towar...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-09-01
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| Series: | Journal of Electrical Systems and Information Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43067-023-00104-2 |
| _version_ | 1797577527554736128 |
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| author | seidu agbor abdul rauf Adebayo F. Adekoya |
| author_facet | seidu agbor abdul rauf Adebayo F. Adekoya |
| author_sort | seidu agbor abdul rauf |
| collection | DOAJ |
| description | Abstract Household lifestyle play a significant role in appliance consumption. The overall effects are that, it can be a determining factor in the healthy functioning of the household appliance or its abnormal functioning. The rapid growth in residential consumption has raised serious concerns toward limited energy resource and high electricity pricing. The propose 134% electricity tariffs adjustment by Electricity Company of Ghana (ECG) at the heat of economic hardships caused by Covid-19 has raised serious public agitation in Ghana (west Africa) . The unpredictable lifestyle of residential consumers in an attempt to attain a comfortable lifestyle and the rippling effects of population growth burdens energy demand at the residential sector. This study attempts to identify the lifestyle factors that have great influence on household energy consumption and predict future consumption of the household with mitigating factors to cushion the effects on high consumption. The study is based on lifestyle data using hybrid machine learning. The hybrid model achieved high accuracy (96%) as compared to previous models. The hybrid model performance was evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE) and correlation coefficient (R) metrics. |
| first_indexed | 2024-03-10T22:09:36Z |
| format | Article |
| id | doaj.art-a613e2c4fc2d40988e94868cfb9c0d6e |
| institution | Directory Open Access Journal |
| issn | 2314-7172 |
| language | English |
| last_indexed | 2024-03-10T22:09:36Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj.art-a613e2c4fc2d40988e94868cfb9c0d6e2023-11-19T12:40:30ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722023-09-0110111810.1186/s43067-023-00104-2Forecasting household energy consumption based on lifestyle data using hybrid machine learningseidu agbor abdul rauf0Adebayo F. Adekoya1Department of Computer Science and Informatics, University of Energy and Natural ResourceDepartment of Computer Science and Informatics, University of Energy and Natural ResourceAbstract Household lifestyle play a significant role in appliance consumption. The overall effects are that, it can be a determining factor in the healthy functioning of the household appliance or its abnormal functioning. The rapid growth in residential consumption has raised serious concerns toward limited energy resource and high electricity pricing. The propose 134% electricity tariffs adjustment by Electricity Company of Ghana (ECG) at the heat of economic hardships caused by Covid-19 has raised serious public agitation in Ghana (west Africa) . The unpredictable lifestyle of residential consumers in an attempt to attain a comfortable lifestyle and the rippling effects of population growth burdens energy demand at the residential sector. This study attempts to identify the lifestyle factors that have great influence on household energy consumption and predict future consumption of the household with mitigating factors to cushion the effects on high consumption. The study is based on lifestyle data using hybrid machine learning. The hybrid model achieved high accuracy (96%) as compared to previous models. The hybrid model performance was evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE) and correlation coefficient (R) metrics.https://doi.org/10.1186/s43067-023-00104-2Hybrid-machine learningLifestyle dataEnergy load forecastingArtificial intelligence |
| spellingShingle | seidu agbor abdul rauf Adebayo F. Adekoya Forecasting household energy consumption based on lifestyle data using hybrid machine learning Journal of Electrical Systems and Information Technology Hybrid-machine learning Lifestyle data Energy load forecasting Artificial intelligence |
| title | Forecasting household energy consumption based on lifestyle data using hybrid machine learning |
| title_full | Forecasting household energy consumption based on lifestyle data using hybrid machine learning |
| title_fullStr | Forecasting household energy consumption based on lifestyle data using hybrid machine learning |
| title_full_unstemmed | Forecasting household energy consumption based on lifestyle data using hybrid machine learning |
| title_short | Forecasting household energy consumption based on lifestyle data using hybrid machine learning |
| title_sort | forecasting household energy consumption based on lifestyle data using hybrid machine learning |
| topic | Hybrid-machine learning Lifestyle data Energy load forecasting Artificial intelligence |
| url | https://doi.org/10.1186/s43067-023-00104-2 |
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