Application of an End-Use Disaggregation Algorithm for Obtaining Building Energy-Use Data

1998 ◽  
Vol 120 (3) ◽  
pp. 205-210 ◽  
Author(s):  
H. Akbari ◽  
S. J. Konopacki
Keyword(s):  
Department Of Defense ◽  
Air Conditioning ◽  
Energy Use ◽  
Short Interval ◽  
Building Energy ◽  
Regression Coefficients ◽  
Electrical Load ◽  
Electricity Use ◽  
Building Energy Use ◽  
End Use

Metered end-use data for most Department of Defense (DoD) facilities are scarce. We discuss the application of a method (end-use disaggregation algorithm, EDA) to disaggregate short-interval (hourly) whole-facility electrical load into major end uses. Hourly load data, hourly load-temperature regression coefficients, and simulated end-use data comprise the algorithm input. The algorithm produces hourly load profiles for air conditioning, lighting, fans and pumps, and miscellaneous loads. In previous studies, the EDA has been applied to both individual and prototypical buildings to estimate energy use by end use for two major California utilities. In this study, we have refined the EDA for application to DoD facilities where typically only the hourly electricity use data are available for the entire facility.

scholarly journals Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3244
Author(s):  
Wenliang Li
Keyword(s):  
New York ◽  
New York City ◽  
Carbon Emissions ◽  
York City ◽  
Energy Use ◽  
Building Energy ◽  
Weather Data ◽  
Electricity Use ◽  
Building Energy Use ◽  
The Government

Building sectors account for major energy use and greenhouse gas emissions in the US. While urban building energy-use modeling has been widely applied in many studies, limited studies have been conducted for Manhattan, New York City (NYC). Since the release of the new “80-by-50” law, the NYC government has committed to reducing carbon emissions by 80% by 2050; indeed, the government is facing a big challenge for reducing the energy use and carbon emissions. Therefore, understanding the building energy use of NYC with a high spatial and temporal resolution is essential for the government and local citizens in managing building energy use. This study quantified the building energy use of Manhattan in NYC with consideration of the local microclimate by integrating two popular modeling platforms, the Urban Weather Generator (UWG) and Urban Building Energy Modeling (UBEM). The research results suggest that (1) the largest building energy use is in central Manhattan, which is composed of large numbers of commercial buildings; (2) a similar seasonal electricity-use pattern and significantly different seasonal gas-use patterns could be found in Manhattan, NYC, due to the varied seasonal cooling and heating demand; and (3) the hourly energy-use profiles suggest only one electricity-use peak in the summer and two gas-use peaks in the winter.

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.

scholarly journals A Rapid Review on Community Connected Microgrids

2021 ◽  
Vol 13 (12) ◽  
pp. 6753
Author(s):  
Moiz Masood Syed ◽  
Gregory M. Morrison
Keyword(s):  
Literature Review ◽  
Urban Areas ◽  
Energy Use ◽  
Residential Buildings ◽  
Optimal Solution ◽  
Building Energy ◽  
Rapid Review ◽  
Solar Pv ◽  
Electricity Cost ◽  
Building Energy Use

As the population of urban areas continues to grow, and construction of multi-unit developments surges in response, building energy use demand has increased accordingly and solutions are needed to offset electricity used from the grid. Renewable energy systems in the form of microgrids, and grid-connected solar PV-storage are considered primary solutions for powering residential developments. The primary objectives for commissioning such systems include significant electricity cost reductions and carbon emissions abatement. Despite the proliferation of renewables, the uptake of solar and battery storage systems in communities and multi-residential buildings are less researched in the literature, and many uncertainties remain in terms of providing an optimal solution. This literature review uses the rapid review technique, an industry and societal issue-based version of the systematic literature review, to identify the case for microgrids for multi-residential buildings and communities. The study describes the rapid review methodology in detail and discusses and examines the configurations and methodologies for microgrids.

scholarly journals The Development of a Biomimetic Design Tool for Building Energy Efficiency

Biomimetics ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 50
Author(s):  
Negin Imani ◽  
Brenda Vale
Keyword(s):  
Focus Group ◽  
Qualitative Data ◽  
Energy Use ◽  
Thermal Adaptation ◽  
Building Energy ◽  
Design Tool ◽  
Building Energy Efficiency ◽  
Energy Efficient Buildings ◽  
Building Energy Use ◽  
Architectural Framework

The initial aim of the research was to develop a framework that would enable architects to look for thermoregulation methods in nature as inspiration for designing energy efficient buildings. The thermo-bio-architectural framework (ThBA) assumes designers will start with a thermal challenge in a building and then look in a systematic way for how this same issue is solved in nature. The tool is thus a contribution to architectural biomimicry in the field of building energy use. Since the ThBA was created by an architect, it was essential that the biology side of this cross-disciplinary tool was validated by experts in biology. This article describes the focus group that was conducted to assess the quality, inclusiveness, and applicability of the framework and why a focus group was selected over other possible methods such as surveys or interviews. The article first provides a brief explanation of the development of the ThBA. Given the focus here is on its validation, the qualitative data collection procedures and analysis results produced by NVivo 12 plus through thematic coding are described in detail. The results showed the ThBA was effective in bridging the two fields based on the existing thermal challenges in buildings, and was comprehensive in terms of generalising biological thermal adaptation strategies.

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.

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