Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature
The aim of this study was to develop an artificial neural network (ANN) prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied) to a target setpoint temperature (when...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-12-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/12/1090 |
| _version_ | 1811186294814932992 |
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| author | Jin Woo Moon Min Hee Chung Hayub Song Se-Young Lee |
| author_facet | Jin Woo Moon Min Hee Chung Hayub Song Se-Young Lee |
| author_sort | Jin Woo Moon |
| collection | DOAJ |
| description | The aim of this study was to develop an artificial neural network (ANN) prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied) to a target setpoint temperature (when a building was occupied). The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1) model development; (2) model optimization; and (3) performance evaluation. Two software programs—Matrix Laboratory (MATLAB) and Transient Systems Simulation (TRNSYS)—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature), presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9), hidden layers (3), moment (0.9), and learning rate (0.9). At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors. |
| first_indexed | 2024-04-11T13:43:10Z |
| format | Article |
| id | doaj.art-90cc2792a5284ea99e3d16aef3ad5aa8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:43:10Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-90cc2792a5284ea99e3d16aef3ad5aa82022-12-22T04:21:10ZengMDPI AGEnergies1996-10732016-12-01912109010.3390/en9121090en9121090Performance of a Predictive Model for Calculating Ascent Time to a Target TemperatureJin Woo Moon0Min Hee Chung1Hayub Song2Se-Young Lee3School of Architecture and Building Science, Chung-Ang University, Seoul 06974, KoreaSchool of Architecture and Building Science, Chung-Ang University, Seoul 06974, KoreaSchool of Architecture and Building Science, Chung-Ang University, Seoul 06974, KoreaSchool of Architecture and Building Science, Chung-Ang University, Seoul 06974, KoreaThe aim of this study was to develop an artificial neural network (ANN) prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied) to a target setpoint temperature (when a building was occupied). The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1) model development; (2) model optimization; and (3) performance evaluation. Two software programs—Matrix Laboratory (MATLAB) and Transient Systems Simulation (TRNSYS)—were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature), presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9), hidden layers (3), moment (0.9), and learning rate (0.9). At the study’s conclusion, the optimized model proved its prediction accuracy with acceptable errors.http://www.mdpi.com/1996-1073/9/12/1090predictive controlsartificial neural network (ANN)setback temperatureascending timeheating system |
| spellingShingle | Jin Woo Moon Min Hee Chung Hayub Song Se-Young Lee Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature Energies predictive controls artificial neural network (ANN) setback temperature ascending time heating system |
| title | Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature |
| title_full | Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature |
| title_fullStr | Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature |
| title_full_unstemmed | Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature |
| title_short | Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature |
| title_sort | performance of a predictive model for calculating ascent time to a target temperature |
| topic | predictive controls artificial neural network (ANN) setback temperature ascending time heating system |
| url | http://www.mdpi.com/1996-1073/9/12/1090 |
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