Modeling heating and cooling loads by regression-based machine learning techniques for energy-efficient building design

Year 2017, , 443 - 449, 30.10.2017

Musa Peker , Osman Özkaraca , Betül Kesimal

https://doi.org/10.17671/gazibtd.310154

Cited By: 8

Abstract

Today,
information technology is used almost in every field. The energy sector is one
of these areas. As the population grew day by day, the number of buildings and
the energy demands of the buildings increased. One way to decrease energy
demand is to design efficient buildings with energy-saving features. In this study,
an analysis of a data set which has eight input values ​​(relative compactness,
surface area, wall area, roof area, overall height, orientation, glazing area and
glazing area distribution) and two output values ​​(heating load (HL), cooling
load (CL)), has been carried out using machine learning algorithms. The aim is
to create a model that predicts the heating and cooling load of the houses.
Accurate estimation of these parameters facilitates controlling of energy
consumption and also helps in selecting an energy supplier that better suits
the energy requirement, which is considered a significant problem in the energy
market. In this context, when analyzing the data set, regression algorithms
(Support Vector Machine (SVM) Regression, Linear Regression, Random Forest
Regression and Nearest Neighbor Regression) are used among machine learning
algorithms. For the two output values, the results have been calculated
experimentally for each algorithm and the results have been compared. For the
data set we analyzed according to the results, the algorithm that found the
closest result in terms of prediction success is Random Forest regression
algorithm.

Keywords

Energy Performance, Regression, SVM Regression, Linear Regression, Random Forest Regression

Enerji Tasarruflu Bina Tasarımı İçin Isıtma ve Soğutma Yüklerini Regresyon Tabanlı Makine Öğrenmesi Algoritmaları İle Modelleme

Abstract

Günümüzde
bilişim teknolojileri hemen hemen her alanda kullanılmaktadır. Enerji sektörü
de bu alanlardan birisidir. Nüfusun gün geçtikçe artmasıyla birlikte bina
sayısı ve binaların enerji talebi de artmıştır. Enerji talebini hafifletmenin
bir yolu enerji tasarrufu özelliklerine sahip verimli binalar tasarlamaktır. Bu
çalışmada sekiz giriş değeri (nispi yoğunluk, yüzey alanı, duvar alanı, çatı
alanı, toplam yükseklik, yönlendirme, cam alanı ve cam alanı dağılımı) ve iki
çıkış değeri (ısıtma yükü (HL), soğutma yükü (CL)) olan bir veri setinin,
makine öğrenmesi algoritmaları kullanarak analizi yapılmıştır. Amaç, konutların
ısıtma ve soğutma yükünü tahmin edebilen bir model oluşturmaktır. Bu
parametrelerin doğru bir şekilde tahmin edilmesi, enerji tüketiminin daha iyi
kontrol edilmesini kolaylaştırmakta ve ayrıca, enerji piyasasında önemli bir
sorun olarak görülen enerji ihtiyacına daha iyi uyan enerji tedarikçisinin
seçiminde yardımcı olmaktadır. Bu kapsamda, veri seti analiz edilirken makine
öğrenmesi algoritmalarından regresyon algoritmaları (Destek Vektör Makinesi (SVM)
Regresyonu, Doğrusal Regresyon, Rasgele Orman Regresyonu ve En Yakın Komşu
Regresyonu) kullanılmıştır. İki çıkış değeri için sonuçlar deneysel olarak her
algoritma için ayrı ayrı hesaplanmış ve elde edilen sonuçlar
karşılaştırılmıştır. Çıkan sonuçlara göre analiz yaptığımız veri seti için,
tahmin başarımı açısından en yakın sonucu bulan algoritma Rastgele Orman
Regresyon algoritması olmuştur.

Keywords

Enerji performansı, Regresyon, SVM regresyon, Lineer regresyon, Rastgele orman regresyon

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