Towards Energy Efficient Home Automation: A Deep Learning Approach
Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the elec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/24/7187 |
| _version_ | 1797544592106586112 |
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| author | Murad Khan Junho Seo Dongkyun Kim |
| author_facet | Murad Khan Junho Seo Dongkyun Kim |
| author_sort | Murad Khan |
| collection | DOAJ |
| description | Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future. |
| first_indexed | 2024-03-10T14:03:40Z |
| format | Article |
| id | doaj.art-212113d1044a45b3a4e740462700b66a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:03:40Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-212113d1044a45b3a4e740462700b66a2023-11-21T00:53:12ZengMDPI AGSensors1424-82202020-12-012024718710.3390/s20247187Towards Energy Efficient Home Automation: A Deep Learning ApproachMurad Khan0Junho Seo1Dongkyun Kim2School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu 41566, KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu 41566, KoreaHome Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.https://www.mdpi.com/1424-8220/20/24/7187smart homesenergy managementInternet of Thingsmachine learning |
| spellingShingle | Murad Khan Junho Seo Dongkyun Kim Towards Energy Efficient Home Automation: A Deep Learning Approach Sensors smart homes energy management Internet of Things machine learning |
| title | Towards Energy Efficient Home Automation: A Deep Learning Approach |
| title_full | Towards Energy Efficient Home Automation: A Deep Learning Approach |
| title_fullStr | Towards Energy Efficient Home Automation: A Deep Learning Approach |
| title_full_unstemmed | Towards Energy Efficient Home Automation: A Deep Learning Approach |
| title_short | Towards Energy Efficient Home Automation: A Deep Learning Approach |
| title_sort | towards energy efficient home automation a deep learning approach |
| topic | smart homes energy management Internet of Things machine learning |
| url | https://www.mdpi.com/1424-8220/20/24/7187 |
| work_keys_str_mv | AT muradkhan towardsenergyefficienthomeautomationadeeplearningapproach AT junhoseo towardsenergyefficienthomeautomationadeeplearningapproach AT dongkyunkim towardsenergyefficienthomeautomationadeeplearningapproach |