Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm
This paper presents a comparative study on the prediction of energy consumption in buildings using machine learning techniques. The dataset encompasses a diverse range of buildings with 8 input features and one output variable, representing the energy consumption. The primary focus is on evaluating...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_01009.pdf |
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| author | El Assri Nasima Ennejjar Mohammed Jallal Mohammed Ali Chabaa Samira Zeroual Abdelouhab |
| author_facet | El Assri Nasima Ennejjar Mohammed Jallal Mohammed Ali Chabaa Samira Zeroual Abdelouhab |
| author_sort | El Assri Nasima |
| collection | DOAJ |
| description | This paper presents a comparative study on the prediction of energy consumption in buildings using machine learning techniques. The dataset encompasses a diverse range of buildings with 8 input features and one output variable, representing the energy consumption. The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). The results indicate a promising predictive capacity of both models, showcasing their effectiveness in capturing intricate patterns within the dataset. In the case of ANN, the Root Mean Squared Error (RMSE) is reported at 3.806, demonstrating the model's ability to approximate the true energy consumption values. Furthermore, the Random Forest model exhibits enhanced predictive accuracy, as reflected by a lower RMSE of 1.392. In addition to predictive analysis, this study utilizes a Modified Whale Optimization Algorithm (MWOA) to optimize energy consumption. The MWOA helps to identify the associated input values that lead to the lowest possible energy consumption, providing valuable insights for energy-efficient building design. The implications of this research extend to the broader field of sustainable architecture and urban planning, paving the way for more informed decisions aimed at reducing energy consumption and fostering environmental sustainability. |
| format | Article |
| id | doaj-art-a8f5fe31b66e4945a19482930702ae43 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-a8f5fe31b66e4945a19482930702ae432025-01-08T10:58:54ZengEDP SciencesITM Web of Conferences2271-20972024-01-01690100910.1051/itmconf/20246901009itmconf_maih2024_01009Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization AlgorithmEl Assri Nasima0Ennejjar Mohammed1Jallal Mohammed Ali2Chabaa Samira3Zeroual Abdelouhab4I2SP Research Team, Physics department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP Research Team, Physics department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP Research Team, Physics department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP Research Team, Physics department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityI2SP Research Team, Physics department, Faculty of Sciences Semlalia, Cadi Ayyad UniversityThis paper presents a comparative study on the prediction of energy consumption in buildings using machine learning techniques. The dataset encompasses a diverse range of buildings with 8 input features and one output variable, representing the energy consumption. The primary focus is on evaluating the performance of two prominent and widely-used machine learning algorithms: Artificial Neural Networks (ANN) and Random Forest (RF). The results indicate a promising predictive capacity of both models, showcasing their effectiveness in capturing intricate patterns within the dataset. In the case of ANN, the Root Mean Squared Error (RMSE) is reported at 3.806, demonstrating the model's ability to approximate the true energy consumption values. Furthermore, the Random Forest model exhibits enhanced predictive accuracy, as reflected by a lower RMSE of 1.392. In addition to predictive analysis, this study utilizes a Modified Whale Optimization Algorithm (MWOA) to optimize energy consumption. The MWOA helps to identify the associated input values that lead to the lowest possible energy consumption, providing valuable insights for energy-efficient building design. The implications of this research extend to the broader field of sustainable architecture and urban planning, paving the way for more informed decisions aimed at reducing energy consumption and fostering environmental sustainability.https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_01009.pdf |
| spellingShingle | El Assri Nasima Ennejjar Mohammed Jallal Mohammed Ali Chabaa Samira Zeroual Abdelouhab Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm ITM Web of Conferences |
| title | Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm |
| title_full | Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm |
| title_fullStr | Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm |
| title_full_unstemmed | Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm |
| title_short | Energy Efficiency in Smart Buildings through Prediction modeling and Optimization Using a Modified Whale Optimization Algorithm |
| title_sort | energy efficiency in smart buildings through prediction modeling and optimization using a modified whale optimization algorithm |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_01009.pdf |
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