Energy behaviours as promoters of energy efficiency: A 21st century review

Lopes, Marta A. R.; Antunes, Carlos Henggeler; Martins, Nélson

doi:10.1016/j.rser.2012.03.034

Cited by 228 publications

(121 citation statements)

References 79 publications

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“…It is noteworthy that the developed energy-modeling and simulation tools for modeling occupants' energy-related characteristics and behaviors (discussed below) are mainly used during the early phase (i.e., design phase) of buildings [73,[76][77][78]. Such tools could help users to choose the correct size and most energy-efficient building systems and the appliances that are proportionate to the number of occupants.…”

Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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Abstract:Buildings currently account for 30-40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States' energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants' energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings' energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants' energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants' energy-consuming behaviors in order to assess and predict such characteristics' impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change OPEN ACCESSEnergies 2015, 8 10997 occupants' energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

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Section: Simulating Occupant Energy-consuming Behaviorsmentioning

confidence: 99%

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

2015

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“…However others have argued that technology adoption does not occur in isolation, and is linked to behaviour in complex ways (Ehrhardt-Martinez, 2012). Other work has demonstrated that the energy savings actually achieved can be highly context dependent (Peattie, 2010); can depend on the modelling approaches used (Lopes et al, 2012); and cross-country differences can be quite pronounced (Mills & Schleich, 2012). …”

Section: Energy Efficiency Versus Curtailment Behaviourmentioning

confidence: 99%

Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour

McCoy

Lyons

2016

Energy Efficiency

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Smart-metering allows electricity utilities to provide consumers with better information on their energy usage and to apply time-of-use pricing. These measures have been shown to reduce electricity consumption and induce time-shifting of demand. Less is known about how they affect residential energy efficiency investment behaviour. We use data from a randomisedcontrolled trial on a sample of almost 2500 Irish consumers, conducted over a 12 month period to investigate the effect of smart-metering and residential feedback on household investment behaviour. The results show that exposure to time-of-use pricing and information stimuli, while reducing overall and peak usage, can also have the unintended effect of reducing investment in energy efficiency measures within the home. Our findings indicate that households exposed to treatment were less likely to adopt any energy saving measure (23-28% on average); and those households adopted less energy saving features than those in the control group (15-21% on average). This result highlights the potential for behavioural interventions to have unintended consequences on behaviours other than those specifically targeted. Furthermore it underlines the importance of examining a wider range of outcomes and allowing longer time-scales when evaluating this type of experiment.Keywords Residential electricity smart-metering · Randomised-controlled trial · Energy efficiency investment IntroductionReducing energy consumption and increasing the adoption of energy saving measures and energy efficient appliances are seen as crucial elements in reducing energy demand. A recent EU directive 1 , aims to "remove barriers and overcome market failures that impede efficiency in the supply and use of energy". The reluctance of users to adopt energy efficient appliances that offer them seemingly positive NPV is known as the "Energy Paradox" and has been widely studied (Blumstein et al

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“…User (occupant) behavior is a major determinant of energy consumption in buildings, particularly in the residential sector [5,6]. The factors that influence the occupant behavior which are related to the building's energy profile form a wide set [7].…”

Section: Techno-human Collective Systems In Smart Gridsmentioning

confidence: 99%

Novel Simulation Approaches for Smart Grids

Tsampasis

Sarakis²,

Leligou³

et al. 2016

JSAN

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Abstract:The complexity of the power grid, in conjunction with the ever increasing demand for electricity, creates the need for efficient analysis and control of the power system. The evolution of the legacy system towards the new smart grid intensifies this need due to the large number of sensors and actuators that must be monitored and controlled, the new types of distributed energy sources that need to be integrated and the new types of loads that must be supported. At the same time, integration of human-activity awareness into the smart grid is emerging and this will allow the system to monitor, share and manage information and actions on the business, as well as the real world. In this context, modeling and simulation is an invaluable tool for system behavior analysis, energy consumption estimation and future state prediction. In this paper, we review current smart grid simulators and approaches for building and user behavior modeling, and present a federated smart grid simulation framework, in which building, control and user behavior modeling and simulation are decoupled from power or network simulators and implemented as discrete components. This framework enables evaluation of the interactions between the communication infrastructure and the power system taking into account the human activities, which are at the focus of emerging energy-related applications that aim to shape user behavior. Validation of the key functionality of the proposed framework is also presented.

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Energy behaviours as promoters of energy efficiency: A 21st century review

Cited by 228 publications

References 79 publications

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour

Novel Simulation Approaches for Smart Grids

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