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

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Alternative Title
Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads
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.
Author(s)
Chanuk LeeDong Eun JungDonghoon Lee김기한Sung Lok Do
Issued Date
2021
Type
Article
Keyword
heating loadartificial neural network modelpredictive modelinput variable
DOI
10.3390/en14030756
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8784
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_848ff63d4e774eb38796d1e7607c5fc0&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Prediction%20Performance%20Analysis%20of%20Artificial%20Neural%20Network%20Model%20by%20Input%20Variable%20Combination%20for%20Residential%20Heating%20Loads&offset=0&pcAvailability=true
Publisher
ENERGIES
Location
스위스
Language
영어
ISSN
1996-1073
Citation Volume
14
Citation Number
3
Citation Start Page
756
Citation End Page
756
Appears in Collections:
Engineering > Architectural Engineering
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