Comparative Analysis of Regression Learning Methods for Estimation of Energy Performance of Residential Structures

Year 2020, Volume: 13 Issue: 2, 600 - 608, 31.08.2020

Abdurrahim Akgundogdu

https://doi.org/10.18185/erzifbed.691398

Cited By: 2

Abstract

Energy efficiency is a top priority for private and commercial buildings. This study evaluates the performance of six regression learning methods, including Linear Regressor, MLP Regressor, RBF Regressor, SVM Regressor, Gaussian Processes, and ANFIS Regressor to predict the heating and cooling loads of residential buildings. 768 buildings were considered and analyzed based on the influential parameters, such as relative density, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution for predicting heating load and cooling load. Three statistical criteria such as correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used to assess the potential of the regression methods used in this study. The best estimation results were obtained with the ANFIS regression model, with R of 0.998, MAE of 0.46 and RMSE of 0.68 for HL; and with R of 0.990, MAE of 1.26 and RMSE of 1.60 for CL.

Keywords

Energy efficiency, heating and cooling loads, regression learning, ANFIS

Supporting Institution

Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa

Project Number

23444

Konut Yapılarında Enerji Performansının Tahmininde Regresyon Öğrenme Yöntemlerinin Karşılaştırmalı Analizi

Abstract

Özel ve ticari binalar için enerji verimliliği birinci önceliktir. Bu çalışma, konut binalarının ısıtma ve soğutma yüklerini tahmin etmek için Lineer Regresör, MLP Regresörü, RBF Regresörü, SVM Regresörü, Gauss İşlemleri ve ANFIS Regresörü dahil olmak üzere altı regresyon öğrenme yönteminin performansını değerlendirmektedir. 768 bina, ısıtma yükü ve soğutma yükünü tahmin etmek için nispi yoğunluk, yüzey alanı, duvar alanı, çatı alanı, toplam yükseklik, yönlendirme, cam alanı ve cam alanı dağılımı gibi etkili parametrelere dayanarak düşünülmüş ve analiz edilmiştir. Bu çalışmada kullanılan regresyon yöntemlerinin potansiyelini değerlendirmek için korelasyon katsayısı (R), ortalama mutlak hata (MAE) ve kök ortalama kare hatası (RMSE) gibi üç istatistiksel kriter kullanılmıştır. En iyi tahmin sonuçları ANFIS regresyon modeli ile; HL için 0.998, MAE 0.46 ve RMSE için 0.68; ve CL için 0.990 R, MAE 1.26 ve RMSE 1.60'tır

Keywords

Enerji verimliliği, ısıtma ve soğutma yükleri, regresyon öğrenme, ANFIS

Project Number

23444

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