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Energies 2016, 9(12), 1090; doi:10.3390/en9121090

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

School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
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Academic Editor: Giovanni Pau
Received: 7 October 2016 / Revised: 7 December 2016 / Accepted: 15 December 2016 / Published: 20 December 2016
(This article belongs to the Special Issue Smart Home Energy Management)
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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. View Full-Text
Keywords: predictive controls; artificial neural network (ANN); setback temperature; ascending time; heating system predictive controls; artificial neural network (ANN); setback temperature; ascending time; heating system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Moon, J.W.; Chung, M.H.; Song, H.; Lee, S.-Y. Performance of a Predictive Model for Calculating Ascent Time to a Target Temperature. Energies 2016, 9, 1090.

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