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Energies 2014, 7(11), 7245-7265; doi:10.3390/en7117245

Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes

1
Department of Building & Plant Engineering, Hanbat National University, Daejeon 305-719, Korea
2
Graduate School of Culture Technology, Korea Advanced Institute of Science & Technology, Daejeon 305-701, Korea
3
Department of Interior Architecture & Built Environment, Yonsei University, Seoul 120-749, Korea
*
Author to whom correspondence should be addressed.
Received: 15 June 2014 / Revised: 27 August 2014 / Accepted: 28 October 2014 / Published: 12 November 2014
(This article belongs to the Special Issue Energy Efficient Building Design and Operation 2014)
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Abstract

This study aims at developing an indoor temperature control method that could provide comfortable thermal conditions by integrating heating system control and the opening conditions of building envelopes. Artificial neural network (ANN)-based temperature control logic was developed for the control of heating systems and openings at the building envelopes in a predictive and adaptive manner. Numerical comparative performance tests for the ANN-based temperature control logic and conventional non-ANN-based counterpart were conducted for single skin enveloped and double skin enveloped buildings after the simulation program was validated by comparing the simulation and the field measurement results. Analysis results revealed that the ANN-based control logic improved the indoor temperature environment with an increased comfortable temperature period and decreased overshoot and undershoot of temperatures outside of the operating range. The proposed logic did not show significant superiority in energy efficiency over the conventional logic. The ANN-based temperature control logic was able to maintain the indoor temperature more comfortably and with more stability within the operating range due to the predictive and adaptive features of ANN models. View Full-Text
Keywords: temperature controls; thermal environment; artificial neural network (ANN); predictive and adaptive control; building envelope; energy efficiency temperature controls; thermal environment; artificial neural network (ANN); predictive and adaptive control; building envelope; energy efficiency
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.; Lee, J.-H.; Kim, S. Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes. Energies 2014, 7, 7245-7265.

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