Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building
This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/6/1434 |
| _version_ | 1797595664213868544 |
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| author | Byeongmo Seo Yeobeom Yoon Kwang Ho Lee Soolyeon Cho |
| author_facet | Byeongmo Seo Yeobeom Yoon Kwang Ho Lee Soolyeon Cho |
| author_sort | Byeongmo Seo |
| collection | DOAJ |
| description | This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency. |
| first_indexed | 2024-03-11T02:40:34Z |
| format | Article |
| id | doaj.art-37cb10b368d44a0f92e07b5445578ffe |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T02:40:34Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-37cb10b368d44a0f92e07b5445578ffe2023-11-18T09:38:15ZengMDPI AGBuildings2075-53092023-05-01136143410.3390/buildings13061434Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office BuildingByeongmo Seo0Yeobeom Yoon1Kwang Ho Lee2Soolyeon Cho3College of Design, North Carolina State University, Raleigh, NC 27695, USABuildings and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USASchool of Architecture, Korea University, Seoul 02481, Republic of KoreaCollege of Design, North Carolina State University, Raleigh, NC 27695, USAThis paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.https://www.mdpi.com/2075-5309/13/6/1434EnergyPlusartificial neural networklong short-term memorydischarged air temperatureoptimal control |
| spellingShingle | Byeongmo Seo Yeobeom Yoon Kwang Ho Lee Soolyeon Cho Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building Buildings EnergyPlus artificial neural network long short-term memory discharged air temperature optimal control |
| title | Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building |
| title_full | Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building |
| title_fullStr | Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building |
| title_full_unstemmed | Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building |
| title_short | Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building |
| title_sort | comparative analysis of ann and lstm prediction accuracy and cooling energy savings through ahu dat control in an office building |
| topic | EnergyPlus artificial neural network long short-term memory discharged air temperature optimal control |
| url | https://www.mdpi.com/2075-5309/13/6/1434 |
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