Optimizing smart home energy management for sustainability using machine learning techniques
Abstract Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenar...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Sustainability |
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| Online Access: | https://doi.org/10.1007/s43621-024-00681-w |
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| author | Muhammad Adnan Khan Zohra Sabahat Muhammad Sajid Farooq Muhammad Saleem Sagheer Abbas Munir Ahmad Tehseen Mazhar Tariq Shahzad Mamoon M. Saeed |
| author_facet | Muhammad Adnan Khan Zohra Sabahat Muhammad Sajid Farooq Muhammad Saleem Sagheer Abbas Munir Ahmad Tehseen Mazhar Tariq Shahzad Mamoon M. Saeed |
| author_sort | Muhammad Adnan Khan |
| collection | DOAJ |
| description | Abstract Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenario presents a unique energy challenge that requires extraordinary efforts to meet the anticipated energy demands. Various innovative strategies are being implemented to overcome the drawbacks and address the growing consumer demand for energy. Today, smart homes offer much more than just basic functions; they also focus on resource management, energy efficiency, and enhancing quality of life. Machine Learning (ML) plays a vital role in smart homes as it allows for the training, adjustment, and optimization of various functions. This intelligent, purposeful capacity has the potential to turn homes into dynamic and practical environments that improve daily performance, ease, and personalization. In this research, an ML-based multivariate model is proposed utilizing Long Short-Term Memory (LSTM) for smart homes, aiming to optimize energy utilization and improve management in the realm of energy consumption. This model offers precise predictions of energy consumption, ensuring minimal random errors. Prominent metrics include a low Mean Squared Error (MSE) of 0.02284, a high Mean Absolute Error (MAE) of 0.184, a Mean Absolute Percentage Error (MAPE) of 0.123, the lowest Root Mean Squared Error (RMSE) at 0.15113, a significant Mean Absolute Scaled Error (MASE) of 0.996, and a strong R-squared value (R2) of 0.694. The proposed model delivers exceptional predictive performance as compared to the previous approaches, ensuring high reliability, which aligns with the standards needed for advancing toward a smart and sustainable future. |
| format | Article |
| id | doaj-art-4129f10f6a554269a693d2447c8bd899 |
| institution | Kabale University |
| issn | 2662-9984 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Sustainability |
| spelling | doaj-art-4129f10f6a554269a693d2447c8bd8992024-12-01T12:06:26ZengSpringerDiscover Sustainability2662-99842024-11-015112410.1007/s43621-024-00681-wOptimizing smart home energy management for sustainability using machine learning techniquesMuhammad Adnan Khan0Zohra Sabahat1Muhammad Sajid Farooq2Muhammad Saleem3Sagheer Abbas4Munir Ahmad5Tehseen Mazhar6Tariq Shahzad7Mamoon M. Saeed8School of Computing, Skyline University College, University City SharjahDepartment of Computer Science, Lahore Garrison UniversityDepartment of Cyber Security, NASTP Institute of Information Technology Lahore (NIIT)School of Computer Science, Minhaj UniversityDepartment of Computer Science, Prince Mohammad Bin Fahd UniversityCollege of Informatics, Korea UniversityDepartment of Computer Science and Information Technology, School Education Department, Government of PunjabDepartment of Computer Science, COMSATS University IslamabadDepartment of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS)Abstract Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenario presents a unique energy challenge that requires extraordinary efforts to meet the anticipated energy demands. Various innovative strategies are being implemented to overcome the drawbacks and address the growing consumer demand for energy. Today, smart homes offer much more than just basic functions; they also focus on resource management, energy efficiency, and enhancing quality of life. Machine Learning (ML) plays a vital role in smart homes as it allows for the training, adjustment, and optimization of various functions. This intelligent, purposeful capacity has the potential to turn homes into dynamic and practical environments that improve daily performance, ease, and personalization. In this research, an ML-based multivariate model is proposed utilizing Long Short-Term Memory (LSTM) for smart homes, aiming to optimize energy utilization and improve management in the realm of energy consumption. This model offers precise predictions of energy consumption, ensuring minimal random errors. Prominent metrics include a low Mean Squared Error (MSE) of 0.02284, a high Mean Absolute Error (MAE) of 0.184, a Mean Absolute Percentage Error (MAPE) of 0.123, the lowest Root Mean Squared Error (RMSE) at 0.15113, a significant Mean Absolute Scaled Error (MASE) of 0.996, and a strong R-squared value (R2) of 0.694. The proposed model delivers exceptional predictive performance as compared to the previous approaches, ensuring high reliability, which aligns with the standards needed for advancing toward a smart and sustainable future.https://doi.org/10.1007/s43621-024-00681-wSustainable energy managementSmart home technologyLSTMHEMSMLMSE |
| spellingShingle | Muhammad Adnan Khan Zohra Sabahat Muhammad Sajid Farooq Muhammad Saleem Sagheer Abbas Munir Ahmad Tehseen Mazhar Tariq Shahzad Mamoon M. Saeed Optimizing smart home energy management for sustainability using machine learning techniques Discover Sustainability Sustainable energy management Smart home technology LSTM HEMS ML MSE |
| title | Optimizing smart home energy management for sustainability using machine learning techniques |
| title_full | Optimizing smart home energy management for sustainability using machine learning techniques |
| title_fullStr | Optimizing smart home energy management for sustainability using machine learning techniques |
| title_full_unstemmed | Optimizing smart home energy management for sustainability using machine learning techniques |
| title_short | Optimizing smart home energy management for sustainability using machine learning techniques |
| title_sort | optimizing smart home energy management for sustainability using machine learning techniques |
| topic | Sustainable energy management Smart home technology LSTM HEMS ML MSE |
| url | https://doi.org/10.1007/s43621-024-00681-w |
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