Improving energy efficiency of buildings using data mining technologies

Zucker, Gerhard; Malinao, Jasmine A.; Habib, Usman; Leber, Thomas; Preisler, Anita; Judex, Florian

doi:10.1109/isie.2014.6865041

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…Clustering algorithms can also be used for finding various energy states in the building, e.g., k-means clustering can be used to detect the state (On/Off ) of machine, as data toggle between these two states Zucker et al 2015a, b). Another example of using clustering for finding system states can be found in Zucker et al (2014), where the X-Means clustering algorithm is used for automatically detecting the system states (On/Off ), in order to examine the operational data of adsorption.…”

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

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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.

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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

Self Cite

View full text Add to dashboard Cite

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“…However, they but did not sufficiently verify whether the rules extracted and as a result the recommendations for changes in the system based on these are actually feasible. Zucker et al [17] successfully used the X-Means algorithm to automate the detection of system states in order to examine operation data of adsorption chillers. Yu and Chan [18] also focused on chillers as a main component of energy consumption and use data envelopment to improve the energy management of chillers.…”

Section: State Of the Artmentioning

confidence: 99%

Sanitation and Analysis of Operation Data in Energy Systems

et al. 2015

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Abstract:We present a workflow for data sanitation and analysis of operation data with the goal of increasing energy efficiency and reliability in the operation of building-related energy systems. The workflow makes use of machine learning algorithms and innovative visualizations. The environment, in which monitoring data for energy systems are created, requires low configuration effort for data analysis. Therefore the focus lies on methods that operate automatically and require little or no configuration. As a result a generic workflow is created that is applicable to various energy-related time series data; it starts with data accessibility, followed by automated detection of duty cycles where applicable. The detection of outliers in the data and the sanitation of gaps ensure that the data quality is sufficient for an analysis by domain experts, in our case the analysis of system energy efficiency. To prove the feasibility of the approach, the sanitation and analysis workflow is implemented and applied to the recorded data of a solar driven adsorption chiller.

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“…For example, clustering can be used to detect the state of machine for ON/OFF cycle as data vary in these two different states. The [20] used the X-Means clustering algorithm for automatically detecting the system states (ON/OFF), to examine the operational data of adsorption. The ON/OFF state information can also be used for finding outliers in the data [21]- [23].…”

Section: State Of the Artmentioning

confidence: 99%

Finding the Different Patterns in Buildings Data Using Bag of Words Representation with Clustering

Habib

Zucker

2015

2015 13th International Conference on Frontiers of Information Technology (FIT)

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The understanding of the buildings operation has become a challenging task due to the large amount of data recorded in energy efficient buildings. Still, today the experts use visual tools for analyzing the data. In order to make the task realistic, a method has been proposed in this paper to automatically detect the different patterns in buildings. The K-Means clustering is used to automatically identify the ON (operational) cycles of the chiller. In the next step the ON cycles are transformed to symbolic representation by using Symbolic Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller. Additionally, the results from the proposed method and dynamic time warping (DTW) approach are also discussed and compared.

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Improving energy efficiency of buildings using data mining technologies

Cited by 10 publications

References 19 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

Sanitation and Analysis of Operation Data in Energy Systems

Finding the Different Patterns in Buildings Data Using Bag of Words Representation with Clustering

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