Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model
In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/22/6419 |
| _version_ | 1797548346907295744 |
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| author | Muhammad Sajjad Samee Ullah Khan Noman Khan Ijaz Ul Haq Amin Ullah Mi Young Lee Sung Wook Baik |
| author_facet | Muhammad Sajjad Samee Ullah Khan Noman Khan Ijaz Ul Haq Amin Ullah Mi Young Lee Sung Wook Baik |
| author_sort | Muhammad Sajjad |
| collection | DOAJ |
| description | In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models. |
| first_indexed | 2024-03-10T14:58:06Z |
| format | Article |
| id | doaj.art-a23e7b76c1f44748b942b4015ea8bc74 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:58:06Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a23e7b76c1f44748b942b4015ea8bc742023-11-20T20:27:26ZengMDPI AGSensors1424-82202020-11-012022641910.3390/s20226419Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output ModelMuhammad Sajjad0Samee Ullah Khan1Noman Khan2Ijaz Ul Haq3Amin Ullah4Mi Young Lee5Sung Wook Baik6Digital Image Processing Laboratory, Islamia College Peshawar, Peshawar 25120, PakistanSejong University, Seoul 143-747, KoreaSejong University, Seoul 143-747, KoreaSejong University, Seoul 143-747, KoreaSejong University, Seoul 143-747, KoreaSejong University, Seoul 143-747, KoreaSejong University, Seoul 143-747, KoreaIn the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.https://www.mdpi.com/1424-8220/20/22/6419cooling loadenergy consumptionenergy efficient buildingGRUheating load |
| spellingShingle | Muhammad Sajjad Samee Ullah Khan Noman Khan Ijaz Ul Haq Amin Ullah Mi Young Lee Sung Wook Baik Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model Sensors cooling load energy consumption energy efficient building GRU heating load |
| title | Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model |
| title_full | Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model |
| title_fullStr | Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model |
| title_full_unstemmed | Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model |
| title_short | Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model |
| title_sort | towards efficient building designing heating and cooling load prediction via multi output model |
| topic | cooling load energy consumption energy efficient building GRU heating load |
| url | https://www.mdpi.com/1424-8220/20/22/6419 |
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