Performance tests on the ANN model prediction accuracy for cooling  load of buildings during the setback period

Park, Bo Rang; Choi, Eunji; Moon, Jin Woo

doi:10.12813/kieae.2017.17.4.083

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…For each training period, the average CvRMSE for the prediction of the energy load was 25%. Park et al [11] proposed an ANN model that can predict the cooling load according to the setback temperature in order to minimize the cooling energy consumption. Their results confirm a CvRMSE of 21.3%, which reflects a better performance than the conventional criterion of 30.0%.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

2019

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This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

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Section: Introductionmentioning

confidence: 99%

Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

2019

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“…In modern times, people tend to spend at least 90% of the day indoors; thus, ecofriendly indoor environments are extremely important [1]. The quality of indoor environments is indicated by the thermal quality, indoor air quality, acoustic quality, light quality, etc.…”

Section: Introductionmentioning

confidence: 99%

Development of Occupant Pose Classification Model Using Deep Neural Network for Personalized Thermal Conditioning

et al. 2019

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This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and optimization of the model. The output of the trained model is the occupant pose of the sedentary activities in the indoor space. The performance of the developed model was evaluated for two different indoor environments: home and office. Using the metabolic rates corresponding to the classified poses, the model accuracy was compared with that of the conventional method, which considered the fixed activity. The result showed that the accuracy was improved by as much as 73.96% and 55.26% in home and office, respectively. Thus, the potential of the pose classification model was verified for providing a more comfortable and personalized thermal environment to the occupant.

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