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

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 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

Investigating the Role of Occupants, Complex Contextual Factors, and Norms on Residential Energy Consumption.

Human behavior in the built environment has repeatedly been found to have significant meaningful impact on energy consumption. As a consequence researchers have spent considerable efforts investigating various approaches to induce improved occupant behavior, with much recent attention on the use of normative approaches. However, it still remains unclear as to how occupants behave in buildings, how complex factors influence behavioral interventions, and what the long term effects of intervening are. With this background in mind, there are three broad goals in this research: (1) to improve our understanding of the impact of occupant decision making in residential energy consumption, (2) to enhance our understanding of how individual characteristics and complex contextual factors influence change in individual behavior and its diffusion through communities when subjected to normative intervention, and (3) to identify more effective normative behavioral strategies for reducing energy consumption in the built environment. In order to achieve these diverse research objectives, I conducted four interrelated studies based on an iterative research framework that applies an interdisciplinary research approach integrating field experiments with computational modeling. Through these studies it was found that: (1) vast quantities of energy are spent in unoccupied residences and that the percentage of energy consumed while unoccupied in a residence is unrelated to total use; (2) when applying behavior interventions social network structure can meaningfully affect how behavior diffuses and intervention outcome; (3) normative messaging duration positively influenced the durability of behavior change; (4) not all individuals were equally influenced by normative messaging with high norm individuals reducing energy consumption and low norm individuals increasing consumption; (5) by exploiting behavioral responses to normative messaging significant reductions in energy consumption could conceptually be achieved. These findings improve our understanding of occupant behavior, how occupants are influenced by social forces in the built environment, and how complex contextual factors moderate the diffusion of behavior. Further, the findings provide insight into how to improve the environmental sustainability of buildings through behavioral approaches.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113508/1/kyleand_1.pd

Of impacts, agents, and functions: An interdisciplinary meta-review of smart home energy management systems research

Smart home energy management technologies (SHEMS) have long been viewed as a promising opportunity to manage the way households use energy. Research on this topic has emerged across a variety of disciplines, focusing on different pieces of the SHEMS puzzle without offering a holistic vision of how these technologies and their users will influence home energy use moving forward. This paper presents the results of a systematic, interdisciplinary meta-review of SHEMS literature, assessing the extent to which it discusses the role of various SHEMS components in driving energy benefits. Results reveal a bias towards technical perspectives and controls approaches that seek to drive energy impacts such as load management and energy savings through SHEMS without user or third-party participation. Not only are techno-centric approaches more common, there is also a lack of integration of these approaches with user-centric, information-based solutions for driving energy impacts. These results suggest future work should investigate more holistic solutions for optimal impacts on household energy use. We hope these results will provoke a broader discussion about how to advance research on SHEMS to capitalize on their potential contributions to demand-side management initiatives moving forward

Twenty questions about design behavior for sustainability, report of the International Expert Panel on behavioral science for design

How behavioral scientists, engineers, and architects can work together to advance how we all understand and practice design—in order to enhance sustainability in the built environment, and beyond.https://www.nature.com/documents/design_behavior_for_sustainability.pdfPublished versio

Market Structure and Energy Efficiency: The Case of New Commercial Buildings

This is a report on why commercial office buildings aren’t more energy efficient. Several decades of energy efficiency programs have resulted in some gains, but overall increases in the energy efficiency of buildings have fallen far short of the 30 to 50 percent improvement that many efficiency advocates believe is possible. The purpose of this study is to consider the “why” question by empirically examining the dynamics of new commercial building markets. To do so, the authors used multiple research techniques, including qualitative field observation and interview methods that allow for a more in-depth understanding of complicated market processes. Their research focused primarily on new office buildings and centered in four regional markets: Sacramento, San Francisco, Seattle, and Portland. The authors identify key dynamics of commercial office building markets, describe how change and innovation occurs in commercial development, discuss the implications for energy efficiency, and suggest next steps

Linking complexity economics and systems thinking, with illustrative discussions of urban sustainability

The expanding research of complexity economics has been signalling its preference for a formal quantitative investigation of diverse interactions between heterogeneous agents at the lower, micro-level resulting in emergent, realistic socioeconomic dynamics at the higher, macro-level. However, there is scarcity in research that explicitly links complexity perspectives in economics with the systems thinking literature, despite these being highly compatible, with strong connections and common historical traces. We aim to address this gap by exploring commonalities and differences between the two bodies of knowledge, seen particularly through an economics lens. We argue for a hybrid approach, in that agent-based complexity perspectives in economics could more closely connect to two main systems thinking attributes: a macroscopic approach to analytically capturing the complex dynamics of systems, and an inter-subjective interpretivist dimension, when investigating complex social-economic order. Illustrative discussions of city sustainability are provided, with an emphasis on decarbonisation and residential energy demand aspects

Bottom-Up Modeling of Building Stock Dynamics - Investigating the Effect of Policy and Decisions on the Distribution of Energy and Climate Impacts in Building Stocks over Time

In Europe, residential and commercial buildings are directly and indirectly responsible for approximately 30–40% of the overall energy demand and emitted greenhouse gas (GHG) emissions. A large share of these buildings was erected before minimum energy-efficiency standards were implemented and are therefore not energy- or carbon-efficient. Consequently, buildings offer significant potential in terms of energy efficiency and the reduction of GHG emissions compared to the status quo. To make use of this potential at scale, targeted policy measures and strategies are needed that should be based on a quantitative assessment of the feasibility and impact of these measures.Building stock models (BSMs) have long been used to assess the current and future energy demand and GHG emissions of building stocks. Most common BSMs characterize the building stock through the use of archetype buildings, which are taken to be representative of large segments of the stock. The increasing availability of disaggregated datasets—such as building registries, 3D city models, and energy performance certificates—has given rise to building-specific BSMs focusing on describing the status quo as an input to energy planning, primarily on the urban scale. Owing to the availability of building-level data, BSMs can be extended beyond policy advice and urban planning, to the assessment of large building portfolios. Thus far, the advances made in building-specific BSMs on the urban scale have not been transferred to the national scale, where such datasets are often not available. Moreover, the focus on an increasingly detailed description of the existing stock has left approaches for modeling stock dynamics without much development. Stock dynamics, therefore, are still primarily modeled through exogenously defined retrofit, demolition, and new construction rates. This limits the applicability and reliability of model results, as the influence of economic, environmental, or policy factors on stock development is not considered.This thesis addresses these shortcomings and advances modeling practices in BSMs. The thesis with appended papers provides a methodology for how the modeling of national building stock can be further developed in terms of building stock characterization through synthetic building stocks as well as stock dynamics through the use of agent-based modeling. Furthermore, the thesis extends BSM applications to inform the strategic planning of large building portfolios through the integration of a maintenance and renovation scheduling method to project the future development of building portfolios

Simulating Network Structure, Layering Multi-layer Network System and Developing Network Block Configuration Model to Understand and Improve Energy Conservation in Residential Buildings

The building sector is a major contributor to total energy consumption in most countries. Traditionally, researchers have focused on leveraging energy efficiency by improving building materials, in-house facilities and transmission equipment. More recently, however, there has been increased focus on research concerning demand-side energy consumption behavior. Current research suggests that energy efficient behavior of a building's occupants can be extensively enhanced through the sharing of energy consumption information among residents in a peer network. However, most of this research relies on experimental tests and does not theorize concepts related to peer network energy efficiency systematically. My dissertation addresses this research gap on two levels. First, I examined if and how the structure of peer networks can impact residents' conservation behaviors through network analysis by employing agent-based simulation techniques. Following confirmation of the impact that network structure has on user behavior, I created a layered network model to integrate information from various network layers and a block configuration model to reconstruct increasingly reliable random networks. In contrast to controlled energy efficiency experiments, real-world networks are large in size, heterogeneous in nature and regularly interact with other networks. By utilizing models developed in this dissertation, we are able to estimate the contribution of network structural coefficients to the energy consumption performance of peer networks. By comparing the layered network and block configuration model I developed with other conventional models, I prove the efficiency, accuracy and reliability of these improved models. These findings have implications for assessing network performance, creating accurate complex random networks for large-scale research, and developing strategies for network design to improve building energy efficiency. This research establishes a system to study residents' energy efficient behaviors from the perspective of peer networks and proposes some instructive models for further energy feedback system design