다층신경망 학습 알고리즘 변화에 따른 건물 냉방부하 예측 모델의 성능 비교 평가 (Comparative Evaluation of Building Cooling Load Prediction Models with Multi-Layer Neural Network Learning Algorithms)

서지정보

· 발행기관 : 한국생태환경건축학회
· 수록지 정보 : KIEAE Journal / 22권 / 4호 / 35 ~ 41페이지
· 저자명 : 성남철, 홍구표

초록

Purpose: In this study, among the methods of applying machine learning when predicting the load of a building, the cooling load of a building was predicted using a neural network model. To investigate the appropriateness of the learning algorithm of the multi-layer neural network model, the main purpose is to compare the predictive performance according to the change in the learning algorithm. Method: Among the learning algorithms applicable to multilayer neural networks, a total of 16 algorithms were used to predict the cooling load and compare the prediction results. The input variables of the input layer of the neural network model are outdoor dry bulb temperature, outdoor humidity, and Seasonally Data. The training period is 70% and the test period was 30%. The number of layers in the hidden layer is 3, the number of neurons is 20, and Epochs is 100. CvRMSE and MBE are used as performance index of the prediction model. The maximum, minimum, average, and standard deviation of the 20 prediction results are calculated, and the prediction performance according to the change in the learning algorithm was compared. Result: As a result of analyzing the predictive performance for each learning algorithm, the predictive performance according to the learning algorithm was different. Considering the results and deviations of the two indicators of predictive performance comprehensively, the model using the Levenberg-Marquardt (LM) learning algorithm is judged to have the best predictive performance.

영어초록

Purpose: In this study, among the methods of applying machine learning when predicting the load of a building, the cooling load of a building was predicted using a neural network model. To investigate the appropriateness of the learning algorithm of the multi-layer neural network model, the main purpose is to compare the predictive performance according to the change in the learning algorithm. Method: Among the learning algorithms applicable to multilayer neural networks, a total of 16 algorithms were used to predict the cooling load and compare the prediction results. The input variables of the input layer of the neural network model are outdoor dry bulb temperature, outdoor humidity, and Seasonally Data. The training period is 70% and the test period was 30%. The number of layers in the hidden layer is 3, the number of neurons is 20, and Epochs is 100. CvRMSE and MBE are used as performance index of the prediction model. The maximum, minimum, average, and standard deviation of the 20 prediction results are calculated, and the prediction performance according to the change in the learning algorithm was compared. Result: As a result of analyzing the predictive performance for each learning algorithm, the predictive performance according to the learning algorithm was different. Considering the results and deviations of the two indicators of predictive performance comprehensively, the model using the Levenberg-Marquardt (LM) learning algorithm is judged to have the best predictive performance.

참고자료

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