Effects of occupant behavior on the energy performance of dwellings

Chapter 3 is a sensitivity analysis conducted using the actual heating behavior data of occupants in the OTB sample. The aim was to model heating behavior and heating energy consumption using Markov chains and Monte Carlo methods. Secondly we wanted to evaluate the robustness of energy consumption of a dwelling to heating behaviors such as thermostat, radiator and ventilation control, as well as presence. The results of this Chapter were compared to Guerra Santin’s work (2010), which analyzes the same data using correlation and regression analyses. This Chapter deals with the Research Question I of this thesis: “Q I. What is the sensitivity of a dwelling’s heating energy consumption to occupant behavior?" The sub-questions are: 1. What are the existing models developed for the occupant behavior and energy performance relationship? and how different are the results of these models in terms of calculating the influence of occupant behavior on energy performance? 2. How can behavior be modelled in order to assess the robustness of the energy performance in dwellings to occupant behavior? 3. What is the weight of each behavioral aspect in terms of its influence on energy consumption?” The research reported in this Chapter was a collaborative work between Harputlugil and Bedir. The data was collected by a questionnaire prepared by Guerra Santin and Bedir, using OTB’s means of data collection. Data organization and initial statistical analysis was done by Bedir, simulations were conducted by Harputlugil and Bedir together, and finally the evalutation of outputs were done by the same authors. The co-author (G. Harputlugil) has given permission to include this paper in this thesis

Modeling Occupant-Building-Appliance Interaction for Energy Waste Analysis

AbstractThe objective of this paper is to discover the emergent energy performance and determinants of energy waste in buildings. Electricity consumption in the U.S. attributes to 73% of energy waste in buildings and much of this waste is due to improper design, operation, and use of appliances. In particular, the operation or use phase of buildings and the way occupants behave significantly contribute to energy waste. Understanding the determinants of energy waste during the operation phase of buildings is a challenging task due to the complex interactions between the occupants, building units, and appliances. To decode these complex interactions and facilitate a better understanding of the determinants of energy waste, a simulation approach is used in this study. An agent-based simulation model was developed to capture the diverse attributes and dynamic behaviors of building occupants at the interface of human-building-appliance interactions. The application of the proposed model is demonstrated in a case study. Using simulation experiments, the interactions between occupant, building unit and appliance on energy consumption were investigated. The simulation model also was used for estimating determinants of energy waste. In addition, the simulation model includes a visualization interface that facilitates communication of strategies between the buildings users and facility managers. The results will highlight the significant attributes and effective strategies for energy waste reduction at the interface of human-building-appliance interactions. This information has potentially significant implications for building designers, facility managers, and users through a better understanding of emergent energy performance of buildings

A systematic literature review using text mining and bibliometric analysis

109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.publishersversionpublishe

A multidisciplinary research approach to energy-related behavior in buildings

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the “performance gap” in the building sector requires a deeper understanding and consideration of the “human factor” in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants’ interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency – to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research

Leveraging Human-environment Systems in Residential Buildings for Aggregate Energy Efficiency and Sustainability

Reducing the energy consumed in the built environment is a key objective in many sustainability initiatives. Existing energy saving methods have consisted of physical interventions to buildings and/or behavioral modifications of occupants. However, such methods may not only suffer from their own disadvantages, e.g. high cost and transient effect, but also lose aggregate energy saving potential due to the oftentimes-associated single-building-focused view and an isolated examination of occupant behaviors. This dissertation attempts to overcome the limitations of traditional energy saving research and practical approaches, and enhance residential building energy efficiency and sustainability by proposing innovative energy strategies from a holistic perspective of the aggregate human-environment systems. This holistic perspective features: (1) viewing buildings as mutual influences in the built environment, (2) leveraging both the individual and contextualized social aspects of occupant behaviors, and (3) incorporating interactions between the built environment and human behaviors. First, I integrate three interlinked components: buildings, residents, and the surrounding neighborhood, and quantify the potential energy savings to be gained from renovating buildings at the inter-building level and leveraging neighborhood-contextualized occupant social networks. Following the confirmation of both the inter-building effect among buildings and occupants' interpersonal influence on energy conservation, I extend the research further by examining the synergy that may exist at the intersection between these "engineered" building networks and "social" peer networks, focusing specifically on the additional energy saving potential that could result from interactions between the two components. Finally, I seek to reach an alignment of the human and building environment subsystems by matching the thermostat preferences of each household with the thermal conditions within their apartment, and develop the Energy Saving Alignment Strategy to be considered in public housing assignment policy. This strategy and the inter-building level energy management strategies developed in my preceding research possess large-scale cost-effectiveness and may engender long-lasting influence compared with existing energy saving approaches. Building from the holistic framework of coupled human-environment systems, the findings of this research will advance knowledge of energy efficiency in the built environment and lead to the development of novel strategies to conserve energy in residential buildings