A Comparative Study Between Thermostat/Hygrometer-Based Conventional and Artificial Neural Network-Based Predictive/Adaptive Thermal Controls in Residential Buildings

Moon, Jin Woo; Han, Seung-Hoon

doi:10.3130/jaabe.11.169

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Previous studies have demonstrated that ANN-based control strategies are superior to mathematical models such as regression models or proportional-integral-derivative (PID) controllers for predicting thermal loads and controlling systems in buildings [13][14][15][16][17][18][19][20][21][22][23]. In particular, ANN-based control strategies provided more comfortable thermal conditions with respect to the air temperature, humidity and predicted mean vote (PMV), which represents the overall thermal quality [24] with reduced overcooling and overheating.…”

Section: Initial Prediction Modelmentioning

confidence: 99%

Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model

Moon

Jung

2016

Energy and Buildings

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Section: Initial Prediction Modelmentioning

confidence: 99%

Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model

Moon

Jung

2016

Energy and Buildings

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“…In previous studies conducted by our research team [12,[14][15][16][17], four thermal control logics were developed to create more comfortable thermal environments for residential buildings. The logics were conventional non-ANN-based temperature and humidity control, ANN-based temperature and humidity control, non-ANN-based predicted mean vote (PMV) control, and ANN-based PMV control.…”

Section: Introductionmentioning

confidence: 99%

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings

2013

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This study examines the performance and adaptability of Artificial Neural Network (ANN)-based thermal control strategies for diverse thermal properties of building envelope conditions applied to residential buildings. The thermal performance using two non-ANN-based control logics and two predictive ANN-based control logics was numerically tested using simulation software after validation. The performance tests were conducted for a two-story single-family house for various envelope insulation levels and window-to-wall ratios on the envelopes. The percentages of the period within the targeted ranges for air temperature, humidity and PMV, and the magnitudes of the overshoots and undershoots outside of the targeted comfort range were analyzed for each control logic scheme. The results revealed that the two predictive control logics that employed thermal predictions of the ANN models achieved longer periods of thermal comfort than the non-ANN-based models in terms of the comfort periods and the reductions of the magnitudes of the overshoots and undershoots. The ANN-based models proved their adaptability through accurate control of the thermal conditions in buildings with various architectural variables. The ANN-based predictive control methods demonstrated their potential to create more comfortable thermal conditions in single-family homes compared to non-ANN based control logics.

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“…reduced overcooling and overheating, and the amount of energy consumption of the heating and cooling systems was significantly reduced [20][21][22][23][24][25][26][27][28][29][30][31][32].…”

mentioning

confidence: 99%

Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms

et al. 2015

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This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse setback temperatures, the most energy-efficient optimal setback temperature could be found and applied in the thermal control logic. Three major processes that used the numerical simulation method were conducted for the development and optimization of an ANN model and for the testing of its prediction performance, respectively. First, the structure and learning method of the initial ANN model was determined to predict the amount of cooling energy consumption during the setback period. Then, the initial structure and learning methods of the ANN model were optimized using parametrical analysis to compare its prediction accuracy levels. Finally, the performance tests of the optimized model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results and the predicted results under generally accepted levels. In conclusion, the proposed ANN model proved its potential to be applied to the thermal control logic for setting up the most energy-efficient setback temperature. OPEN ACCESSEnergies 2015, 8 8227

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A Comparative Study Between Thermostat/Hygrometer-Based Conventional and Artificial Neural Network-Based Predictive/Adaptive Thermal Controls in Residential Buildings

Cited by 8 publications

References 8 publications

Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model

Algorithm for optimal application of the setback moment in the heating season using an artificial neural network model

Determining Adaptability Performance of Artificial Neural Network-Based Thermal Control Logics for Envelope Conditions in Residential Buildings

Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms

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