Applying principal component analysis and weighted support vector machine in building cooling load forecasting

Jinhu, Lv; Li, Xuemei; Ding, Lixing; Jiang, Li

doi:10.1109/cctae.2010.5543476

Cited by 13 publications

(10 citation statements)

References 5 publications

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“…In [20], the authors created several regression models to estimate the anticipated heat load of a residential single-family section in a moderate climate each month. Work in [21] developed a forecasting model that combines principal component analysis (PCA) to extract the essential features, and a weighted support vector regression model to predict cooling demand.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings

et al. 2019

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Reducing energy loads while maintaining the degree of hotness and coldness plays an essential role in designing energy-efficient buildings. Some previous methods have been proposed for predicting building energy loads using traditional machine learning methods. However, these traditional methods suffer from overfitting problems, which leads to inaccurate prediction results. To achieve high accuracy results, an ensemble learning approach is proposed in this paper. The proposed approach uses an extreme gradient boosting (XGBoost) algorithm to avoid overfitting problems and builds an efficient prediction model. An extensive experiment is conducted on a selected dataset of residential building designs to evaluate the proposed approach. The dataset consists of 768 samples of eight input attributes (overall height, relative compactness, wall area, surface area, roof area, glazing area distribution, glazing area, and orientation) and two output responses (cooling load (CL) and heating load (HL)). The experimental results prove that the proposed approach achieves the highest prediction performance, which will help building managers and engineers make better decisions regarding building energy loads.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings

et al. 2019

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“…Bir giriş dizi vektörü 1 , 2 , 3 , … , ∈ ( < ) ve ∑ =1 = 0 ise [11,14], vektörlerin kovaryans matrisi denklem (1)'deki gibi hesaplanır:…”

Section: Temel Bileşen Analiziunclassified

“…Burada; ƛ , , k ve , sırasıyla C kovaryasının öz değeri, öz vektörlerin özdeşi, öz vektörlerin en büyüğü ve eşik değeri en büyük k öz vektörünün yaklaşık duyarlılığını ifade eder [11,14].…”

Section: Temel Bileşen Analiziunclassified

Saldırı Tespiti için Makine Öğrenme Yöntemlerinin Karşılaştırmalı Analizi

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2021

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Özİnternet ve ağ teknolojilerindeki hızlı gelişmeler, siber saldırılar ve izinsiz erişimlerin başta olduğu birçok dezavantajı beraberinde getirmektedir. Bu girişimlerin önceden tespiti, olası saldırıların gerçekleşmeden önlenebilmesini sağlamaktadır. Bu çalışmada makine öğrenme yaklaşımlarının saldırı tespiti üzerindeki performansları araştırılmıştır. Tüm deneyler, açık erişime sunulmuş ve yaygın olarak kullanılan KDD'99 veri kümesi altındaki KDD10CORRECTED ve KDDTEST setleri üzerinde gerçekleştirilmiştir. Sınıflandırıcı olarak, KA, TÖ ve DVM tercih edilmiştir. Veri setleri hem doğrudan sınıflandırıcıların girişi olarak hem de boyut indirgeme tekniği olan TBA uygulanarak sınıflandırılmıştır. Sınıflandırma aşamasında 5-kat çapraz doğrulama tekniği kullanılmıştır. En iyi başarım oranları KDD10CORRECTED veri setinde üzerinde %99,99 ile Torbalama sınıflandırıcısı, KDDTEST veri setinde üzerinde %97,90 ile Torbalama sınıflandırıcısı, KDD10CORRECTED+KDDTEST veri setinde üzerinde %100 ile Torbalama sınıflandırıcısı elde etmiştir. Elde edilen sonuçlar kıyaslanarak rapor edilmiştir. Sonuçlar gelecekteki çalışmalar için cesaret vericidir.Anahtar kelimeler: Saldırı tespiti, topluluk öğrenme, destek vektör makinesi, karar ağacı, temel bileşen analizi.

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“…This approach contributes to learning correction for limited training sets and enhanced prediction time efficiency to traditional SVM model in load forecasting. Jinhu et al 2010 and apply improved PCA to find the significant parameters and show better accuracy. However, the information about original and selected features are missing.…”

Section: Support Vector Machinementioning

confidence: 99%

Machine learning for estimation of building energy consumption and performance: a review

et al. 2018

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Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

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Applying principal component analysis and weighted support vector machine in building cooling load forecasting

Cited by 13 publications

References 5 publications

An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings

An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings

Saldırı Tespiti için Makine Öğrenme Yöntemlerinin Karşılaştırmalı Analizi

Machine learning for estimation of building energy consumption and performance: a review

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