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

Assessment of Energy Savings Potential from the Use of Demand Controlled Ventilation in General Office Spaces in California

A prototypical office building meeting the prescriptive requirements of the 2008 California building energy efficiency standards (Title 24) was used in EnergyPlus simulations to calculate the energy savings potential of demand controlled ventilation (DCV) in five typical California climates per three design occupancy densities and two minimum ventilation rates. The assumed minimum ventilation rates in offices without DCV, based on two different measurement methods employed in a large survey, were 38 and 13 L/s per occupant. The results of the life cycle cost analysis show DCV is cost effective for office spaces if the typical minimum ventilation rate without DCV is 38 L/s per person, except at the low design occupancy of 10.8 people per 100 m2 in climate zones 3 (north coast) and 6 (south Coast). DCV was not found to be cost effective if the typical minimum ventilation rate without DCV is 13 L/s per occupant, except at high design occupancy of 21.5 people per 100 m2 in climate zones 14 (desert) and 16 (mountains). Until the large uncertainties about the base case ventilation rates in offices without DCV are reduced, the case for requiring DCV in general office spaces will be a weak case. Under the Title 24 Standards office occupant density of 10.8 people per 100 m2, DCV becomes cost effective when the base case minimum ventilation rate is greater than 42.5, 43.0, 24.0, 19.0, and 18.0 L/s per person for climate zone 3, 6, 12, 14, and 16 respectively

EnergyPlus Run Time Analysis

EnergyPlus is a new generation building performance simulation program offering many new modeling capabilities and more accurate performance calculations integrating building components in sub-hourly time steps. However, EnergyPlus runs much slower than the current generation simulation programs. This has become a major barrier to its widespread adoption by the industry. This paper analyzed EnergyPlus run time from comprehensive perspectives to identify key issues and challenges of speeding up EnergyPlus: studying the historical trends of EnergyPlus run time based on the advancement of computers and code improvements to EnergyPlus, comparing EnergyPlus with DOE-2 to understand and quantify the run time differences, identifying key simulation settings and model features that have significant impacts on run time, and performing code profiling to identify which EnergyPlus subroutines consume the most amount of run time. This paper provides recommendations to improve EnergyPlus run time from the modeler?s perspective and adequate computing platforms. Suggestions of software code and architecture changes to improve EnergyPlus run time based on the code profiling results are also discussed

EnergyPlus Analysis Capabilities for Use in California Building Energy Efficiency Standards Development and Compliance Calculations

California has been using DOE-2 as the main building energy analysis tool in the development of building energy efficiency standards (Title 24) and the code compliance calculations. However, DOE-2.1E is a mature program that is no longer supported by LBNL on contract to the USDOE, or by any other public or private entity. With no more significant updates in the modeling capabilities of DOE-2.1E during recent years, DOE-2.1E lacks the ability to model, with the necessary accuracy, a number of building technologies that have the potential to reduce significantly the energy consumption of buildings in California. DOE-2's legacy software code makes it difficult and time consuming to add new or enhance existing modeling features in DOE-2. Therefore the USDOE proposed to develop a new tool, EnergyPlus, which is intended to replace DOE-2 as the next generation building simulation tool. EnergyPlus inherited most of the useful features from DOE-2 and BLAST, and more significantly added new modeling capabilities far beyond DOE-2, BLAST, and other simulations tools currently available. With California's net zero energy goals for new residential buildings in 2020 and for new commercial buildings in 2030, California needs to evaluate and promote currently available best practice and emerging technologies to significantly reduce energy use of buildings for space cooling and heating, ventilating, refrigerating, lighting, and water heating. The California Energy Commission (CEC) needs to adopt a new building energy simulation program for developing and maintaining future versions of Title 24. Therefore, EnergyPlus became a good candidate to CEC for its use in developing and complying with future Title 24 upgrades. In 2004, the Pacific Gas and Electric Company contracted with ArchitecturalEnergy Corporation (AEC), Taylor Engineering, and GARD Analytics to evaluate EnergyPlus in its ability to model those energy efficiency measures specified in both the residential and nonresidential Alternative Calculation Method (ACM) of the Title-24 Standards. The AEC team identified gaps between EnergyPlus modeling capabilities and the requirements of Title 24 and ACMs. AEC's evaluation was based on the 2005 version of Title 24 and ACMs and the version 1.2.1 of EnergyPlus released on October 1, 2004. AEC's evaluation is useful for understanding the functionality and technical merits of EnergyPlus for implementing the performance-based compliance methods described in the ACMs. However, it did not study the performance of EnergyPlus in actually making building energy simulations for both the standard and proposed building designs, as is required for any software program to be certified by the CEC for use in doing Title-24 compliance calculations. In 2005, CEC funded LBNL to evaluate the use of EnergyPlus for compliance calculations by comparing the ACM accuracy test runs between DOE-2.1E and EnergyPlus. LBNL team identified key technical issues that must be addressed before EnergyPlus can be considered by the CEC for use in developing future Nonresidential Title-24 Standards or as an ACM tool. With Title 24 being updated to the 2008 version (which adds new requirements to the standards and ACMs), and EnergyPlus having been through several update cycles from version 1.2.1 to 2.1, it becomes crucial to review and update the previously identified gaps of EnergyPlus for use in Title 24, and more importantly to close the gaps which would help pave the way for EnergyPlus to be adopted as a Title 24 compliance ACM. With this as the key driving force, CEC funded LBNL in 2008 through this PIER (Public Interest Energy Research) project with the overall technical goal to expand development of EnergyPlus to provide for its use in Title-24 standard compliance and by CEC staff

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