Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads

Chanuk Lee, Dong Eun Jung, Donghoon Lee, Kee Han Kim and Sung Lok Do
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Chanuk Lee: Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea
Dong Eun Jung: Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea
Donghoon Lee: Department of Architectural Engineering, Hanbat University, Daejeon 34158, Korea
Kee Han Kim: Department of Architectural Engineering, Ulsan University, Ulsan 44610, Korea
Sung Lok Do: Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea

Energies, 2021, vol. 14, issue 3, 1-19

Abstract: In Korea apartment buildings, most energy is consumed as heating energy. In order to reduce heating energy in apartment buildings, it is required to reduce the amount of energy used in heating systems. Energy saving in heating systems can be achieved through operation and control based on efficient operation plans. The efficient operation plan of the heating system should be based on the predicted heating load. Thus, various methods have been developed for predicting heating loads. Recently, artificial intelligence techniques (e.g., ANN: artificial neural network) have been used to predict heating loads. The process for determination of input data variables is necessary to obtain the accuracy of predicted results using an ANN model. However, there is a lack of studies to evaluate the accuracy level of the predicted results caused by the selection and combination of input variables. There is a need to evaluate the performance of an ANN model for prediction of residential heating loads. Therefore, the purpose of this study is, for a residential building, to evaluate the accuracy levels of predicted heating loads using an ANN model with various combinations of input variables. To achieve the study purpose, each case was classified according to the combination of the input variables and the prediction results were analyzed. Through this, the worst, mean, and best were selected according to the predicted performance. In addition, an actual case was selected consisting of variables that can be measured in an actual building. The derived cv(RMSE) of each case resulted in a percentage value of 38.2% for the worst, 7.3% for the mean, 3.0% for the best, and 5.4% for the actual. The largest difference between the best and worst resulted in 33.2%, and thus the precision of the predicted heating loads was highly affected by the selection and combination of the input variables used for the ANN model.

Keywords: heating load; artificial neural network model; predictive model; input variable (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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