Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage
The increasing Internet of Things (IoT) device integration in smart home environments has increased the options available for intelligent energy management. In the context of smart homes, this paper provides a detailed analysis on the use of IoT data for energy consumption trend prediction and anoma...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2025-01-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Online Access: | https://publications.muet.edu.pk/index.php/muetrj/article/view/3291 |
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| _version_ | 1841561222301876224 |
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| author | Laviza Falak Naz Rohail Qamar Raheela Asif Saman Hina Muhammad Imran Saad Ahmed |
| author_facet | Laviza Falak Naz Rohail Qamar Raheela Asif Saman Hina Muhammad Imran Saad Ahmed |
| author_sort | Laviza Falak Naz |
| collection | DOAJ |
| description | The increasing Internet of Things (IoT) device integration in smart home environments has increased the options available for intelligent energy management. In the context of smart homes, this paper provides a detailed analysis on the use of IoT data for energy consumption trend prediction and anomaly detection. We propose a novel approach that combines the advantages of the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for accurate consumption forecasting. Real-world data from a smart home setting is utilised to evaluate the proposed models. Results will therefore show that our approach performs best in optimally utilizing resources, minimizing waste, and improving energy consumption. The current study contributes to the development of energy-efficient smart houses through providing a reliable method for consumption forecasting and anomaly detection. Results indicate that the LSTM model outperformed ARIMA in prediction accuracy, achieving a lower Mean Absolute Error (MAE) of 0.110 compared to ARIMA's 0.176. Furthermore, the LSTM model demonstrated superior performance in anomaly detection, with higher precision and recall scores. |
| format | Article |
| id | doaj-art-dba4c0a2a19a405e9d94b453fe716c60 |
| institution | Kabale University |
| issn | 0254-7821 2413-7219 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj-art-dba4c0a2a19a405e9d94b453fe716c602025-01-03T05:23:58ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192025-01-0144111312310.22581/muet1982.32913291Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usageLaviza Falak Naz0Rohail Qamar1Raheela Asif2Saman Hina3Muhammad Imran4Saad Ahmed5Department of Software Engineering, NED University of Engineering TechnologyDepartment of Computer Science Information Technology, NED University of Engineering & TechnologyDepartment of Software Engineering, NED University of Engineering TechnologyDepartment of Computer Science Information Technology, NED University of Engineering & TechnologyDepartment of Computer Science Information Technology, NED University of Engineering & TechnologyDepartment of Computer Science, IQRA UniversityThe increasing Internet of Things (IoT) device integration in smart home environments has increased the options available for intelligent energy management. In the context of smart homes, this paper provides a detailed analysis on the use of IoT data for energy consumption trend prediction and anomaly detection. We propose a novel approach that combines the advantages of the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for accurate consumption forecasting. Real-world data from a smart home setting is utilised to evaluate the proposed models. Results will therefore show that our approach performs best in optimally utilizing resources, minimizing waste, and improving energy consumption. The current study contributes to the development of energy-efficient smart houses through providing a reliable method for consumption forecasting and anomaly detection. Results indicate that the LSTM model outperformed ARIMA in prediction accuracy, achieving a lower Mean Absolute Error (MAE) of 0.110 compared to ARIMA's 0.176. Furthermore, the LSTM model demonstrated superior performance in anomaly detection, with higher precision and recall scores.https://publications.muet.edu.pk/index.php/muetrj/article/view/3291 |
| spellingShingle | Laviza Falak Naz Rohail Qamar Raheela Asif Saman Hina Muhammad Imran Saad Ahmed Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage Mehran University Research Journal of Engineering and Technology |
| title | Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage |
| title_full | Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage |
| title_fullStr | Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage |
| title_full_unstemmed | Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage |
| title_short | Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage |
| title_sort | intelligent energy management in iot enabled smart homes anomaly detection and consumption prediction for energy efficient usage |
| url | https://publications.muet.edu.pk/index.php/muetrj/article/view/3291 |
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