Monitoring System for Residential Energy Consumption

Bouktif, Salah; Ahmed, Waleed

doi:10.17781/p001983

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…For example, Spitler (2006) discussed the use of model-based control systems as an application of DDDAS and the need to explore methods of data-gathering processes under a DDDAS framework. Bouktif and Ahmed (2015) discussed the application of DDDAS to the energy consumption of a residential system, where the application includes real-time metering and sensor data and incorporates human decisions into energy analytics to enhance predictions. In current building performance applications, DDDAS fits well for applications such as real-time model-based simulations for optimizing building operations (Pang et al 2016).…”

Section: Related Studiesmentioning

confidence: 99%

Open Computing Infrastructure for Sharing Data Analytics to Support Building Energy Simulations

Karaguzel

Elshambakey

Zhu

et al. 2019

J. Comput. Civ. Eng.

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Building energy simulation plays an increasingly important role in building design and operation. In this paper, we present an open computing infrastructure, Virtual Information Fabric Infrastructure (VIFI), that allows building designers and engineers to enhance their simulations by combining empirical data with diagnostic or prognostic models. Based on the idea of dynamic data-driven application systems (DDDAS), the VIFI infrastructure complements conventional data-centric sharing strategies and addresses key data sharing concerns such as the privacy of building occupants. To demonstrate the potential of the VIFI infrastructure, we simulate an empirically-derived lighting schedule in the U.S. Department of Energy's small office building reference model. We use the case study simulation to explore the possibility and potential of integrating data-centric and analytic-centric sharing strategies; the method of combining empirical data with simulations; the creation, sharing, and execution of analytics using VIFI; and the impact of incorporating empirical data on energy simulations. While the case study reveals clear advantages of the VIFI data infrastructure, research questions remain surrounding the motivation and benefits for sharing data, the metadata that are required to support the composition of analytics, and the performance metrics that could be used in assessing the applications of VIFI.

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Section: Related Studiesmentioning

confidence: 99%

Open Computing Infrastructure for Sharing Data Analytics to Support Building Energy Simulations

Karaguzel

Elshambakey

Zhu

et al. 2019

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

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Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

2022

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There are two primary ways to save energy within a building: (1) through improving building engineering structures and adopting efficient appliance ownership, and (2) through changing occupants’ energy-consuming behaviors. Unfortunately the second way suffers from many challenges and limitations. Occupant behavior is, indeed, a complex and multi-disciplinary concept depending on several human factors. Although its importance is recognized by the energy management community, it is often oversimplified and naively defined when used to study, analyze or model energy load. This paper aims at promoting the definition of occupant behavior as well as exploring the extent to which the latter is involved in research works, targeting directly or indirectly energy savings. Hence, in this work, we propose an overview of interdisciplinary research approaches that consider occupants’ energy-saving behaviors, while we present the big picture and evaluate how occupant behavior is defined, we also propose a categorization of the major works that consider energy-consuming occupant behavior. Our findings via a literature review methodology, based on a bibliometric study, reveal a growth of the number of research works involving occupant behavior to model load forecasting and household segmentation. We have equally identified a research trend showing an increasing interest in studying how to successfully change occupant behaviors towards energy saving.

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Monitoring System for Residential Energy Consumption

Cited by 2 publications

References 24 publications

Open Computing Infrastructure for Sharing Data Analytics to Support Building Energy Simulations

Open Computing Infrastructure for Sharing Data Analytics to Support Building Energy Simulations

Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview

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