Using intelligent data analysis to detect abnormal energy consumption in buildings

Seem, John E.

doi:10.1016/j.enbuild.2006.03.033

Cited by 178 publications

(82 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are several machine learning techniques that can be used for extracting information from the data, e.g. clustering can be used for finding similar daily performance patterns in the buildings (Miller et al 2015;Seem 2005), detecting the abnormal performance from electricity consumption Seem (2007), and further enhancing the performance optimization algorithms (Kusiak and Song 2008). Moreover, at a larger scale, wavelet transformations and clustering can be used for the classification of electrical demand profiles of buildings (Florita et al 2013).…”

Section: Other Methods For Analysis Of Energy Systems In Buildingsmentioning

confidence: 99%

Complex building’s energy system operation patterns analysis using bag of words representation with hierarchical clustering

Habib

Hayat

Zucker

2016

Complex Adapt Syst Model

View full text Add to dashboard Cite

Purpose Due to the large quantity of data that are recorded in energy efficient buildings, understanding the behavior of various underlying operations has become a complex and challenging task. This paper proposes a method to support analysis of energy systems and validates it using operational data from a cold water chiller. The method automatically detects various operation patterns in the energy system. Methods The use of k-means clustering is being proposed to automatically identify the On (operational) cycles of a system operating with a duty cycle. The latter’s data is subsequently transformed to symbolic representations by using the symbolic aggregate approximation method. Afterward, the symbols are converted to bag of words representation (BoWR) for hierarchical clustering. A gap statistics method is used to find the best number of clusters in the data. Finally, operation patterns of the energy system are grouped together in each cluster. An adsorption chiller, operating under real life conditions, supplies the reference data for validation. Results The proposed method has been compared with dynamic time warping (DTW) method using cophenetic coefficients and it has been shown that the BoWR has produced better results as compared to DTW. The results of BoWR are further investigated and for finding the optimal number of clusters, gap statistics have been used. At the end, interesting patterns of each cluster are discussed in detail. Conclusion The main goal of this research work is to provide analysis algorithms that automatically find the various patterns in the energy system of a building using as little configuration or field knowledge as possible. A bag of word representation method with hierarchical clustering has been proposed to assess the performance of a building energy system.

show abstract

Section: Other Methods For Analysis Of Energy Systems In Buildingsmentioning

confidence: 99%

Complex building’s energy system operation patterns analysis using bag of words representation with hierarchical clustering

Habib

Hayat

Zucker

2016

Complex Adapt Syst Model

View full text Add to dashboard Cite

show abstract

“…As an example the tested abnormal conditions were as follows: the recirculation damper stuck, cooling coil fooling and supply fan speed decreasing and the fault indicators were found to be for example the supply air temperature, mixed air temperature, outlet water temperature. In the second paper [18], the same kind of work is performed. The paper's aim consists in preventing peak loads.…”

Section: Model Derivation Strategiesmentioning

confidence: 99%

“…In the control field, namely in fault detection diagnosis of energy systems, recent research [17,18] shows a particular interest in data mining tools. The first article introduces a method for the detection of abnormal running condi-tions in a HVAC (Heating Ventilating and Air Conditioning) system with the following process: first a comparison between reference temperature signals in normal conditions, calculated from a thermal modeling, and real signals, then a classification of the transformed residual signals with a multi-layer SVM classifier.…”

Section: Model Derivation Strategiesmentioning

confidence: 99%

Vapnik's learning theory applied to energy consumption forecasts in residential buildings

Lai¹,

Magoulès²,

Lherminier³

2008

International Journal of Computer Mathematics

View full text Add to dashboard Cite

For the purpose of energy conservation, we present in this paper an introduction to the use of Support Vector (SV) Learning Machines used as a data mining tool applied to buildings energy consumption data from a measurement campaign. Experiments using a SVM-based software tool for the prediction of the electrical consumption of a residential building is performed. The data included one year and three months of daily recordings of electrical consumption and climate data such as temperatures and humidities. The learning stage was done for a first part of the data, the predictions were done for the last month. Performances of the model and contributions of significant factors were also derived. The results show good performances for the model. Besides the second experiment consists in model re-estimations on a one-year daily recording dataset lagged at one-day time intervals in such a way that we derive temporal series of influencing factors weights along with model performance criteria. Finally we introduce a perturbation in one of the influencing variable to detect a model change. Comparing contributing weights with and without the perturbation, the sudden contributing weight change could have diagnosed the perturbation. The important point is the ease of the production of many models. This method announces future research work in the exploitation of possibilities of this "model factory".

show abstract

“…This method can detect abnormal patterns with low frequency, but ignores more common patterns with anomalously high fluctuation. Other efforts [18,19] first extract the features from daily energy consumption then use statistical methods to identify abnormally high or low energy use. However, these methods relied on the assumption that the data is sampled from a particular distribution, which may not hold true.…”

Section: Related Workmentioning

confidence: 99%

The user side of sustainability: Modeling behavior and energy usage in the home

Chen

Cook

Crandall

2013

Pervasive and Mobile Computing

View full text Add to dashboard Cite

Using intelligent data analysis to detect abnormal energy consumption in buildings

Cited by 178 publications

References 5 publications

Complex building’s energy system operation patterns analysis using bag of words representation with hierarchical clustering

Complex building’s energy system operation patterns analysis using bag of words representation with hierarchical clustering

Vapnik's learning theory applied to energy consumption forecasts in residential buildings

The user side of sustainability: Modeling behavior and energy usage in the home

Contact Info

Product

Resources

About