scholarly journals Investigating the Impact of Actual and Modeled Occupant Behavior Information Input to Building Performance Simulation

Buildings ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Mengda Jia ◽  
Ravi Srinivasan ◽  
Robert J. Ries ◽  
Gnana Bharathy ◽  
Nathan Weyer
Keyword(s):  
Energy Use ◽  
Building Design ◽  
System Optimization ◽  
Building Energy ◽  
Building Performance ◽  
Occupant Behavior ◽  
Commercial Building ◽  
Multiple Perspectives ◽  
Human In The Loop ◽  
The Impact

Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization.

Evaluation of the Impact of Window Shading on the Anti-Insulation Phenomenon in Building Energy Use

MRS Advances ◽  
2018 ◽  
Vol 3 (34-35) ◽  
pp. 2063-2073
Author(s):  
R. K. Rabasoma ◽  
D. D. Serame ◽  
O.T. Masoso
Keyword(s):  
Solar Radiation ◽  
Energy Use ◽  
Common Knowledge ◽  
Building Design ◽  
Building Energy ◽  
Climatic Regions ◽  
Building Energy Use ◽  
The World ◽  
Building Design Decisions ◽  
The Impact

ABSTRACTBefore 2008, it was common knowledge around the world that insulation always saved air conditioning energy in buildings. In 2008 a phenomenon called anti-insulation was brought to light by Masoso & Grobler. They demonstrated that there are instances when insulation materials in a building directly increase building energy use. Researchers around the world then echoed the message. Recent work by some of the authors investigated the anti-insulation phenomenon in summer and winter for both hot climatic regions (Botswana) and cold climatic regions (Canada). Their study concluded that there is still a mystery of exaggerated sources of heat inside the building aggravating the anti-insulation phenomenon. They hypothesized that incident solar radiation through the windows could be one of the causes. This paper therefore focuses on eliminating direct solar radiation through windows by applying external shadings on a previously anti-insulation building. The energy saved is evaluated and the possible reversal of anti-insulation studied. The study is useful to energy policy makers and the building industry as it showcases the existence of a possible silent killer (anti-insulation) and demonstrates that investing large sums of money on insulation in buildings may not be the most economic thing to do in building design decisions.

scholarly journals How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2502
Author(s):  
Jacqueline Nicole Adams ◽  
Zsófia Deme Bélafi ◽  
Miklós Horváth ◽  
János Balázs Kocsis ◽  
Tamás Csoknyai
Keyword(s):  
Western Europe ◽  
Energy Use ◽  
Building Energy ◽  
Energy Performance ◽  
Smart Meter ◽  
Occupant Behavior ◽  
Psychological Determinants ◽  
Data Limitations ◽  
Holistic Understanding ◽  
The Impact

The goal of this literature review was to outline the research currently conducted on smart meter (SM) adoption and its connection to building occupant behavior to better understand both SM technology and SM customers. We compiled our findings from the existing literature and developed a holistic understanding of the socio-demographic factors that lead to more or less energy use, the methods used to group and cluster occupants on the basis of energy use, how occupant energy use profiles are developed, and which socio-psychological determinants may influence SM adoption. Our results highlight 11 demographic variables that impact building energy use, find 9 methods commonly used to profile occupants on the basis of energy usage, and highlight 13 socio-psychological variables than can be utilized to better understand SM adoption intentions. The review findings two major deficiencies in the existing literature. First, this review highlights the lack of existing interdisciplinary research that combines occupant behavior with SM data and a clear socio-psychological framework. Second, this review underscores certain data limitations in existing SM research, with most research being conducted only on residential or office buildings and geographically in North America or Western Europe. Final policy recommendations center on increased need for interdisciplinary SM research and the need for an expanded understanding of occupant behavior and SM research across different geographies.

scholarly journals Proposed method for probabilistic risk analysis using building performance simulations and stochastic parameters

2020 ◽  
Vol 172 ◽  
pp. 25005
Author(s):  
Tomas Ekström ◽  
Stephen Burke ◽  
Lars-Erik Harderup ◽  
Jesper Arfvidsson
Keyword(s):  
Risk Analysis ◽  
Energy Demand ◽  
Energy Use ◽  
Building Design ◽  
Energy Performance ◽  
Probabilistic Methods ◽  
Building Performance ◽  
Single Family ◽  
Stochastic Parameters ◽  
The Impact

As parts of the world continue the work of mitigating the impact of climate change, many countries strive for continued reductions in energy demand from buildings by implementing more stringent building regulations. Consequently, the importance of accurate and efficient building performance simulations to predict the energy use of a building design increases. As observed in earlier studies, there are performance gaps between the predicted annual energy demand from building energy performance simulations based on deterministic methods compared to the monitored annual energy use of a building. This paper presents a preliminary method developed using probabilistic methods for risk analysis and building performance simulations to predict the energy performance of buildings using stochastic parameters. The method is used to calculate the probability for the energy performance of a building design to fulfil the energy requirements. The consequences are quantified using an example of energy performance contracting to evaluate the inherent risk of a building’s design. The method was demonstrated in a case study and validated by comparing the results in energy performance and probability of failure against measured data from 26 single-family houses.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

Cost Optimization of Net-Zero Energy House

2007 ◽  
Author(s):  
George A. Mertz ◽  
Gregory S. Raffio ◽  
Kelly Kissock
Keyword(s):  
Energy Use ◽  
Cost Optimization ◽  
Building Design ◽  
Building Energy ◽  
Zero Energy ◽  
Resource Limitations ◽  
Net Zero Energy ◽  
Net Zero ◽  
Building Energy Use ◽  
The Cost

Environmental and resource limitations provide increased motivation for design of net-zero energy or net-zero CO2 buildings. The optimum building design will have the lowest lifecycle cost. This paper describes a method of performing and comparing lifecycle costs for standard, CO2-neutral and net-zero energy buildings. Costs of source energy are calculated based on the cost of photovoltaic systems, tradable renewable certificates, CO2 credits and conventional energy. Building energy simulation is used to determine building energy use. A case study is conducted on a proposed net-zero energy house. The paper identifies the least-cost net-zero energy house, the least-cost CO2 neutral house, and the overall least-cost house. The methodology can be generalized to different climates and buildings. The method and results may be of interest to builders, developers, city planners, or organizations managing multiple buildings.

Energy Efficiency Design Strategy of Commercial Building in Northeast Cold Regions

2012 ◽  
Vol 174-177 ◽  
pp. 2165-2169
Author(s):  
Yao Fu ◽  
Tian Heng Zhang
Keyword(s):  
Architectural Design ◽  
Building Energy ◽  
Point Of View ◽  
Commercial Building ◽  
Design Strategies ◽  
Cold Regions ◽  
Location Selection ◽  
Shape Coefficient ◽  
Energy Evaluation ◽  
The Impact

From the point of view of architectural design, envelope location, selection, and identify programs of envelope structure in the modern commercial building, give priority to the establishment of image of shopping malls , creating the mood of commercial and other factors. The paper will establish the appropriate model to the impact of the shape coefficient of Commercial building energy consumption in cold regions, validity analysis used the building energy evaluation software named Autodesk Ecotect to provide adequate theoretical basis of energy conservation design strategies.

scholarly journals An Optimization-Based Supervisory Control and Coordination Approach for Solar-Load Balancing in Building Energy Management

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1215
Author(s):  
James Allen ◽  
Ari Halberstadt ◽  
John Powers ◽  
Nael H. El-Farra
Keyword(s):  
Solar Energy ◽  
Energy Use ◽  
Weather Forecasting ◽  
Energy Generation ◽  
Building Energy ◽  
Objective Functions ◽  
Forecasting Accuracy ◽  
Electricity Cost ◽  
The Impact ◽  
Building Loads

This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management of the building’s energy generation-load balance in an effort to increase the feasibility of wide-scale deployment and integration of solar power generation into commercial buildings. To realize this goal, a simulated building model that accounts for on-site solar energy generation, battery storage, electrical vehicle (EV) charging, controllable lighting, and air conditioning is considered, and a supervisory model predictive control (MPC) system is developed to coordinate the building’s generation, loads and storage systems. The main aim of this optimization-based approach is to find a reasonable solution that minimizes the economic cost to the electricity user, while at the same time reducing the impact of the building loads on the grid. To assess this goal, three objective functions are selected, including the peak building load, the net building energy use, and a weighted sum of both the peak load and net energy use. Based on these objective functions, three MPC systems are implemented on the simulated building under scenarios with varying degrees of weather forecasting accuracy. The peak demand, energy cost, and electricity cost are compared for various forecast scenarios for each MPC system formulation, and evaluated in relation to a rules-based control scheme. The MPC systems tested the rules-based scheme based on simulations of a month-long electricity consumption. The performance differences between the individual MPC system formulations are discussed in the context of weather forecasting accuracy, operational costs, and how these impact the potential of on-site solar generation and potential wide-spread solar penetration.

Sign in / Sign up

Export Citation Format

Share Document