Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings
In this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildings, some of which have the property of symmetry. F...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/8/1553 |
| _version_ | 1797431693077905408 |
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| author | Bita Ghasemkhani Reyat Yilmaz Derya Birant Recep Alp Kut |
| author_facet | Bita Ghasemkhani Reyat Yilmaz Derya Birant Recep Alp Kut |
| author_sort | Bita Ghasemkhani |
| collection | DOAJ |
| description | In this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildings, some of which have the property of symmetry. For this purpose, it proposes novel machine learning models that were built by using the tri-layered neural network (TNN) and maximum relevance minimum redundancy (MRMR) algorithms. Each feature related to buildings was investigated in terms of skewness to determine whether their distributions are symmetric or asymmetric. The best features were determined as the essential parameters for energy consumption. The results of this study show that the properties of relative compactness and glazing area have the most impact on energy consumption in the buildings, while orientation and glazing area distribution are less correlated with the output variables. In addition, the best mean absolute error (MAE) was calculated as 0.28993 for heating load (kWh/m<sup>2</sup>) prediction and 0.53527 for cooling load (kWh/m<sup>2</sup>) prediction, respectively. The experimental results showed that our method outperformed the state-of-the-art methods on the same dataset. |
| first_indexed | 2024-03-09T09:48:52Z |
| format | Article |
| id | doaj.art-6b7523f7e0b44ab1962ae8a3c461eece |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T09:48:52Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-6b7523f7e0b44ab1962ae8a3c461eece2023-12-02T00:21:28ZengMDPI AGSymmetry2073-89942022-07-01148155310.3390/sym14081553Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart BuildingsBita Ghasemkhani0Reyat Yilmaz1Derya Birant2Recep Alp Kut3Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Izmir 35390, TurkeyIn this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildings, some of which have the property of symmetry. For this purpose, it proposes novel machine learning models that were built by using the tri-layered neural network (TNN) and maximum relevance minimum redundancy (MRMR) algorithms. Each feature related to buildings was investigated in terms of skewness to determine whether their distributions are symmetric or asymmetric. The best features were determined as the essential parameters for energy consumption. The results of this study show that the properties of relative compactness and glazing area have the most impact on energy consumption in the buildings, while orientation and glazing area distribution are less correlated with the output variables. In addition, the best mean absolute error (MAE) was calculated as 0.28993 for heating load (kWh/m<sup>2</sup>) prediction and 0.53527 for cooling load (kWh/m<sup>2</sup>) prediction, respectively. The experimental results showed that our method outperformed the state-of-the-art methods on the same dataset.https://www.mdpi.com/2073-8994/14/8/1553machine learningInternet of thingsenergy consumptionsmart buildingstri-layered neural networkscooling load |
| spellingShingle | Bita Ghasemkhani Reyat Yilmaz Derya Birant Recep Alp Kut Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings Symmetry machine learning Internet of things energy consumption smart buildings tri-layered neural networks cooling load |
| title | Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings |
| title_full | Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings |
| title_fullStr | Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings |
| title_full_unstemmed | Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings |
| title_short | Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings |
| title_sort | machine learning models for the prediction of energy consumption based on cooling and heating loads in internet of things based smart buildings |
| topic | machine learning Internet of things energy consumption smart buildings tri-layered neural networks cooling load |
| url | https://www.mdpi.com/2073-8994/14/8/1553 |
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