Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

Florita, Anthony; Brackney, Larry; Otanicar, Todd; Robertson, Jeffrey

doi:10.1115/1.4024029

Cited by 16 publications

(8 citation statements)

References 7 publications

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“…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

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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

View full text Add to dashboard Cite

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“…This field seeks to group various types of buildings or meters into similar clusters for analysis [11][12][13][14][15][16][17][18]. Various studies have looked at classification of building with various objectives using temporal meter data as a source of features [19][20][21]16,22]. Several other studies have extracted temporal features that enhance the ability to forecast consumption [23][24][25].…”

Section: Previous Workmentioning

confidence: 99%

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Miller

Meggers

2017

Energy and Buildings

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This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6% more accurate for primary building use type, 24.3% more accurate for performance class, and 63.6% more accurate for building operations type as compared to baselines.

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“…Once the wavelet coefficients are calculated, the signal energy may be calculated for each resolution level (equation ( 6)) 14 as well as the total signal energy (equation ( 7)). 14…”

Section: Background On Clustering Approachesmentioning

confidence: 99%

“…In addition to signal energy, the signal entropy can also be calculated (equation (8)). 14 The DWT-based clustering approached relied on the K-means clustering of the signal's energy and entropy to produce clusters. Unlike the ACFC approach described earlier, the DWT-based clustering is a crisp clustering approach.…”

Section: Background On Clustering Approachesmentioning

confidence: 99%

A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand

Inman

Elmore

Bush

2015

Building Services Engineering Research and Technology

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Building performance data are widely used for daily operation, improving building efficiency, identifying and diagnosing performance problems, and commissioning. In this study, the authors explore the use of missing data imputation and clustering on an electrical demand dataset. The objective was to compare four approaches of data imputation and clustering analysis. Results of this study suggest that using multiple imputation to fill in missing data prior to performing clustering analysis results in more informative clusters. Commonly used methods to fill in missing data lead to changes in cluster membership that are not suggestive of a change in the building's performance, but instead is a result of the choice of imputation method used. Practical application: The authors demonstrate, through the use of a case study, the application of a statistically sound method for filling in missing data in large buildings performance datasets. The methods used in this analysis are available through the open-source programming language R and are straight forward to implement. The approach demonstrated in this case study could aid buildings analysts with fault detection and continuous commissioning of large commercial buildings.

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Classification of Commercial Building Electrical Demand Profiles for Energy Storage Applications

Cited by 16 publications

References 7 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

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

A case study to examine the imputation of missing data to improve clustering analysis of building electrical demand

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