Usage profiles for sustainable buildings

Vazquez, Felix Iglesias; Kästner, Wolfgang

doi:10.1109/etfa.2010.5641214

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…• In [15], control based on usage profiles is presented under the coverage of an integrative and global approach: ThinkHome. A controller prototype is depicted widely.…”

Section: Related Work and Previous Stepsmentioning

confidence: 99%

Impact of user habits in smart home control

2011

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Lifestyle and habits of users have a direct effect on the energy performance of dwellings and facilities. Hence, in the built environment, advanced control strategies must adapt to user behaviors trying to keep a commitment between energy consumption and comfort requirements. In previous works, the suitability of predictive control based on occupancy profiles for the optimization of HVAC systems has been shown. Resting upon this basis, the present work performs a sensitivity analysis for control strategies based on usage profiles, where the input under variation is the level of habit-regularity of users. Therefore, different hypothetical user models are created and tested. The results of this analysis provide a better understanding of how user behavior affects the energy and comfort performance in dwellings under smart control and paves the way for enhanced controller design.

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“…• In [15], control based on usage profiles is presented under the coverage of an integrative and global approach: ThinkHome. A controller prototype is depicted widely.…”

Section: Related Work and Previous Stepsmentioning

confidence: 99%

Impact of user habits in smart home control

2011

Self Cite

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“…In parallel, profiles allow a control system to guess the next steps in advance (e.g. [7], [17], [8]). Indeed, there is a high sensitivity in the user behavior or energy usage parameters, minor variations result in considerable differences.…”

Section: Behaviour Profiles and Uncertainties In Their Discoverymentioning

confidence: 99%

“…In previous applications, we have tested their flexibility, accuracy, local minima and variable density management capabilities in comparison with other methods [9]. XSOM [8] 1 tends to overperform SOM solutions mainly when the deployment of clustering tools is intended for pattern discovery. Remote samples which are considered outliers for XSOM are classified by SOM without any distinction.…”

Section: Exclusive Som (Xsom)mentioning

confidence: 99%

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Behavioral Profiles for Building Energy Performance Using eXclusive SOM

Vazquez

Gaceo

Kästner

et al. 2011

Engineering Applications of Neural Networks

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Abstract. The identification of user and usage profiles in the built environment is of vital importance both for energy performance analysis and smart control purposes. Clustering tools are a suitable means as they are able to discover representative patterns from a myriad of collected data. In this work, the methodology of an eXclusive Self-Organizing Map (XSOM) is proposed as an evolution of a Kohonen map with outlier rejection capabilities. As will be shown, XSOM characteristics fit perfectly with the targeted application areas.

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“…A substantial amount of effort has been put into the gas demand forecasting [11][12][13][14][15][16] and into the determinant factors of residential gas consumption [17,18]. The study of energy savings has been conducted for buildings, as well, mostly inserted in projects for the development of sustainable cities [19,20]. The investigation of the weather impact on energy consumption is also addressed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Residential Gas Consumers Using Clustering Analysis

et al. 2017

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Abstract:The growing environmental concerns and liberalization of energy markets have resulted in an increased competition between utilities and a strong focus on efficiency. To develop new energy efficiency measures and optimize operations, utilities seek new market-related insights and customer engagement strategies. This paper proposes a clustering-based methodology to define the segmentation of residential gas consumers. The segments of gas consumers are obtained through a detailed clustering analysis using smart metering data. Insights are derived from the segmentation, where the segments result from the clustering process and are characterized based on the consumption profiles, as well as according to information regarding consumers' socio-economic and household key features. The study is based on a sample of approximately one thousand households over one year. The representative load profiles of consumers are essentially characterized by two evident consumption peaks, one in the morning and the other in the evening, and an off-peak consumption. Significant insights can be derived from this methodology regarding typical consumption curves of the different segments of consumers in the population. This knowledge can assist energy utilities and policy makers in the development of consumer engagement strategies, demand forecasting tools and in the design of more sophisticated tariff systems.

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Usage profiles for sustainable buildings

Cited by 11 publications

References 21 publications

Impact of user habits in smart home control

Impact of user habits in smart home control

Behavioral Profiles for Building Energy Performance Using eXclusive SOM

Segmentation of Residential Gas Consumers Using Clustering Analysis

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