A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes
Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7974 |
| _version_ | 1797576967051018240 |
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| author | Quentin Meteier Mira El Kamali Leonardo Angelini Omar Abou Khaled |
| author_facet | Quentin Meteier Mira El Kamali Leonardo Angelini Omar Abou Khaled |
| author_sort | Quentin Meteier |
| collection | DOAJ |
| description | Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents’ consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations. |
| first_indexed | 2024-03-10T22:01:16Z |
| format | Article |
| id | doaj.art-3fad78f6d46b416db0fcce021eb621f1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:01:16Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3fad78f6d46b416db0fcce021eb621f12023-11-19T12:56:53ZengMDPI AGSensors1424-82202023-09-012318797410.3390/s23187974A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart HomesQuentin Meteier0Mira El Kamali1Leonardo Angelini2Omar Abou Khaled3HumanTech Institute, University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, SwitzerlandHumanTech Institute, University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, SwitzerlandHumanTech Institute, University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, SwitzerlandHumanTech Institute, University of Applied Sciences and Arts Western Switzerland (HES-SO), 1700 Fribourg, SwitzerlandPhotovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents’ consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations.https://www.mdpi.com/1424-8220/23/18/7974energy consumptionhome automationmachine learningrecommender systemsmart homesolar production |
| spellingShingle | Quentin Meteier Mira El Kamali Leonardo Angelini Omar Abou Khaled A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes Sensors energy consumption home automation machine learning recommender system smart home solar production |
| title | A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes |
| title_full | A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes |
| title_fullStr | A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes |
| title_full_unstemmed | A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes |
| title_short | A Recommender System for Increasing Energy Efficiency of Solar-Powered Smart Homes |
| title_sort | recommender system for increasing energy efficiency of solar powered smart homes |
| topic | energy consumption home automation machine learning recommender system smart home solar production |
| url | https://www.mdpi.com/1424-8220/23/18/7974 |
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