Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature

Jin Woo Moon, Min Hee Chung, Hayub Song and Se-Young Lee
Additional contact information
Jin Woo Moon: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
Min Hee Chung: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
Hayub Song: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
Se-Young Lee: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea

Energies, 2016, vol. 9, issue 12, 1-16

Abstract: 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.

Keywords: predictive controls; artificial neural network (ANN); setback temperature; ascending time; heating system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/9/12/1090/pdf (application/pdf)
https://www.mdpi.com/1996-1073/9/12/1090/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:9:y:2016:i:12:p:1090-:d:85618

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().