A technical framework to describe occupant behavior for building energy simulations

ABSTRACT Green buildings that fail to meet expected design performance criteria indicate that technology alone does not guarantee high performance. Human influences are quite often simplified and ignored in the design, construction, and operation of buildings. Energy-conscious human behavior has been demonstrated to be a significant positive factor for improving the indoor environment while reducing the energy use of buildings. In our study we developed a new technical framework to describe energyrelated human behavior in buildings. The energy-related behavior includes accounting for individuals and groups of occupants and their interactions with building energy services systems, appliances and facilities. The technical framework consists of four key components: i. the drivers behind energy-related occupant behavior, which are biological, societal, environmental, physical, and economical in nature ii. the needs of the occupants are based on satisfying criteria that are either physical (e.g. thermal, visual and acoustic comfort) or non-physical (e.g. entertainment, privacy, and social reward) iii. the actions that building occupants perform when their needs are not fulfilled iv. the systems with which an occupant can interact to satisfy their needs The technical framework aims to provide a standardized description of a complete set of human energyrelated behaviors in the form of an XML schema. For each type of behavior (e.g., occupants opening/closing windows, switching on/off lights etc.) we identify a set of common behaviors based on a literature review, survey data, and our own field study and analysis. Stochastic models are adopted or developed for each type of behavior to enable the evaluation of the impact of human behavior on energy use in buildings, during either the design or operation phase. We will also demonstrate the use of the technical framework in assessing the impact of occupancy behavior on energy saving technologies. The technical framework presented is part of our human behavior research, a 5-year program under the

Data Mining of Occupant Behavior in Office Buildings

Literature studies confirm occupant behavior is setting the direction for contemporary researches aiming to bridge the gap between predicted and actual energy performance of sustainable buildings. Using the Knowledge Discovery in Database (KDD) methodology, two data mining learning processes are proposed to extrapolate office occupancy and windows’ operation behavioral patterns from a two-years data set of 16 offices in a natural ventilated office building. Clustering procedures, decision tree models and rule induction algorithms are employed to obtain association rules segmenting the building occupants into working user profiles, which can be further implemented as occupant behavior advanced-inputs into building energy simulations

A technical framework to describe occupant behavior for building energy simulations

ABSTRACT Green buildings that fail to meet expected design performance criteria indicate that technology alone does not guarantee high performance. Human influences are quite often simplified and ignored in the design, construction, and operation of buildings. Energy-conscious human behavior has been demonstrated to be a significant positive factor for improving the indoor environment while reducing the energy use of buildings. In our study we developed a new technical framework to describe energyrelated human behavior in buildings. The energy-related behavior includes accounting for individuals and groups of occupants and their interactions with building energy services systems, appliances and facilities. The technical framework consists of four key components: i. the drivers behind energy-related occupant behavior, which are biological, societal, environmental, physical, and economical in nature ii. the needs of the occupants are based on satisfying criteria that are either physical (e.g. thermal, visual and acoustic comfort) or non-physical (e.g. entertainment, privacy, and social reward) iii. the actions that building occupants perform when their needs are not fulfilled iv. the systems with which an occupant can interact to satisfy their needs The technical framework aims to provide a standardized description of a complete set of human energyrelated behaviors in the form of an XML schema. For each type of behavior (e.g., occupants opening/closing windows, switching on/off lights etc.) we identify a set of common behaviors based on a literature review, survey data, and our own field study and analysis. Stochastic models are adopted or developed for each type of behavior to enable the evaluation of the impact of human behavior on energy use in buildings, during either the design or operation phase. We will also demonstrate the use of the technical framework in assessing the impact of occupancy behavior on energy saving technologies. The technical framework presented is part of our human behavior research, a 5-year program under the

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models.

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking

End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

States and utilities are developing increasingly ambitious energy goals. Part of the solution to meeting these goals is improving electric grid flexibility. This includes shifting electric demand to align with grid needs. Thus, identifying and using building energy efficiency and other distributed energy resources to produce the highest grid value requires highly resolved, accurate and accessible electricity end-use load profiles (EULPs). EULPs quantify how and when energy is used. Currently, few accurate and accessible end-use load profiles are available for utilities, public utility commissions, state energy offices and other stakeholders to use to prioritize investment and value energy efficiency, demand response, distributed generation and energy storage. High-quality EULPs are also critical for determining the time-sensitive value of efficiency and other distributed energy resources, and the widespread adoption of grid-interactive efficient buildings (GEBs).For example, EULPs can be used to accurately forecast energy savings in buildings or identify energy activities that can be shifted to different times of the day. This report serves as the first-year deliverable for a multiyear U.S. Department of Energy-funded project, End-Use Load Profiles for the U.S. Building Stock, that intends to produce a set of highly resolved EULPs of the U.S. residential and commercial building stock. The project team, made up of researchers from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory, ultimately will use calibrated physics-based building energy models to create these EULPs

Advanced Simulation Methods for Occupant-Centric Building Design

Performance quantification through simulation has been particularly advantageous to building design, as it can be applied to non-existent buildings in the design process, allows for testing design variants under identical conditions, and demands much less resources as compared to physical measurements. Consequently, use of building simulation in the design process has evolved to – for example – establish and verify design performance, screen and optimize design parameters, and study robustness and adaptability in adverse conditions. In this context, the present chapter investigates how the state-of-the-art simulation-aided design procedures can contribute to realize occupant-centric design objectives. To this end, the chapter, first, discusses the ways in which simulation-aided design methods can represent occupants and capture their interactions with buildings’ environmental control systems. Subsequently, a number of key simulation-aided design methods and objectives are explored with a focus on the role of occupants. Finally, a carefully described prototypical building model serves to demonstrate and test the introduced occupant-centric simulation-aided design procedures

Italian prototype building models for urban scale building performance simulation

Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. The developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals