Development of an RDP neural network for building energy consumption fault detection and diagnosis

Magoulès, Frédéric; Zhao, Hai‐Xiang; Elizondo, David

doi:10.1016/j.enbuild.2013.02.050

Cited by 77 publications

(26 citation statements)

References 26 publications

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“…Zhao and Magoul es [38] has the most comprehensive literature review but they too find little previous work concerned with feature selection, and their subsequent approach is to apply support vector regression to select subsets of features to predict energy consumption. Other methods have been used to select or filter variables in particular situations, including principal components analysis of energy use [39], and neural networks and data mining methods to detect outliers [40] and faults [41].…”

Section: Variable Selection As a Key Analysis Issuementioning

confidence: 99%

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Hsu

2015

Energy

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a b s t r a c tStatistical models can only be as good as the data put into them. Data about energy consumption continues to grow, particularly its non-technical aspects, but these variables are often interpreted differently among disciplines, datasets, and contexts. Selecting key variables and interactions is therefore an important step in achieving more accurate predictions, better interpretation, and identification of key subgroups for further analysis.This paper therefore makes two main contributions to the modeling and analysis of energy consumption of buildings. First, it introduces regularization, also known as penalized regression, for principled selection of variables and interactions. Second, this approach is demonstrated by application to a comprehensive dataset of energy consumption for commercial office and multifamily buildings in New York City. Using cross-validation, this paper finds that a newly-developed method, hierarchical grouplasso regularization, significantly outperforms ridge, lasso, elastic net and ordinary least squares approaches in terms of prediction accuracy; develops a parsimonious model for large New York City buildings; and identifies several interactions between technical and non-technical parameters for further analysis, policy development and targeting. This method is generalizable to other local contexts, and is likely to be useful for the modeling of other sectors of energy consumption as well.

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Section: Variable Selection As a Key Analysis Issuementioning

confidence: 99%

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Hsu

2015

Energy

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“…Fortunately, there are many works in which the authors also use NNs as the basis of their investigations and promising results are obtained. In an energy consumption context, Magoulès et al [27] diagnose different electrical equipment of an office building, including fans, pumps, cooling equipment, and chillers. They use a recursive deterministic perceptron NN to distinguish between normal and defective datasets, where an effectiveness percentage higher than 97% is obtained.…”

Section: Classification-based Methodsmentioning

confidence: 99%

“…NNs and rule sets [26] Recursive deterministic perceptron NN [27] Feedforward NN [28] Hebbian NN [29] B-spline membership fuzzy NN [30] ANFIS [31,52] Feed forward NN [32][33][34] Multi-layer NN [35] Modular NNs [36] Adaptive linear NN and feed forward NN [37] Neural-fuzzy network and statistical analysis [38] Refrigeration…”

Section: Classification Methods Equipment Under Test Physical Variablmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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“…Their results showed that the GRNN model is an accurate and reliable estimator for highly nonlinear and complex AHU processes. Magoulès et al [19] developed a recursive deterministic perceptron (RDP) neural network for FDD at the building level. Based on their results which showed a higher than 97% generalization performance, they proposed a new diagnostic architecture that reported both the source of the faults and their degradation likelihood.…”

Section: Related Workmentioning

confidence: 99%

Robust model-based fault diagnosis for air handling units

Mulumba¹,

Afshari²,

Yan³

et al. 2015

Energy and Buildings

110

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Development of an RDP neural network for building energy consumption fault detection and diagnosis

Cited by 77 publications

References 26 publications

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Robust model-based fault diagnosis for air handling units

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