Exploring the impact of office building users' modeling approaches on energy use across Canadian climates

2019 ◽  
Vol 197 ◽  
pp. 68-86 ◽  
Author(s):  
Sara Gilani ◽  
William O'Brien
Keyword(s):  
Energy Use ◽  
Office Building ◽  
Modeling Approaches ◽  
The Impact

Roof Cover Impact on Cooling Energy Use of Office Buildings

2014 ◽  
Author(s):  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Energy Savings ◽  
Simulation Analysis ◽  
Calculation Model ◽  
Office Building ◽  
Climate Zones ◽  
Building Energy Use ◽  
Detailed Simulation ◽  
Simplified Calculation ◽  
The Impact

This paper analyzes the impact of roof covers on office building energy use for representative US climate zones. In particular, the study presented in the paper investigates the potential annual cooling energy use savings that roof covers could provide using whole-building simulation analysis to evaluate the performance of a 2-story office building in five US locations. Three parameters of the roof covers including their size, height, and transmittance, are considered in the analysis. The simulation results indicate that while roof covers had similar affects on buildings in all climate zones, their impact in reducing cooling energy usage is different and is more pronounced in cooler climates. Specifically, roof covers could potentially achieve cooling energy savings of up to: 25% in Houston, 33% in Atlanta, 31% in Nashville, 38% in Chicago, and 41% in Madison. Based on the detailed simulation analysis results, a simplified calculation model is developed to help the estimation of cooling energy savings as a function of the roof cover size, height, and transmittance.

Analysis of Electrical Energy Savings From Daylighting Through Skylights

Solar Energy ◽  
2005 ◽  
Author(s):  
Abdelkarim Nemri ◽  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Energy Savings ◽  
Simulation Analysis ◽  
Control Strategies ◽  
Electrical Energy ◽  
Office Building ◽  
Analysis Tool ◽  
Energy Saving Potential ◽  
Lighting Energy ◽  
The Impact

This paper provides a simplified analysis tool to assess the energy saving potential of daylighting for commercial buildings through skylights. Specifically, the impact of daylighting is investigated for various fenestration opening sizes, glazing types, control strategies, and geographic locations. A top floor of a prototypical office building has been considered in the analysis. The results obtained for the office building can be applied to other types of buildings such as retails stores, schools, and warehouses. Based on the simulation analysis results, it was determined that skylight to floor ratio more than 0.3 does not affect significantly the lighting energy savings. An optimum value of skylight to floor area ratio was found to be 0.2 to minimize the annual total building energy use.

scholarly journals Office Building Tenants’ Electricity Use Model for Building Performance Simulations

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5541
Author(s):  
Andrea Ferrantelli ◽  
Helena Kuivjõgi ◽  
Jarek Kurnitski ◽  
Martin Thalfeldt
Keyword(s):  
Energy Use ◽  
Office Building ◽  
Building Performance ◽  
Substantial Portion ◽  
Critical Importance ◽  
Simple Method ◽  
Heating And Cooling ◽  
Electricity Use ◽  
The Impact

Large office buildings are responsible for a substantial portion of energy consumption in urban districts. However, thorough assessments regarding the Nordic countries are still lacking. In this paper we analyse the largest dataset to date for a Nordic office building, by considering a case study located in Stockholm, Sweden, that is occupied by nearly a thousand employees. Distinguishing the lighting and occupants’ appliances energy use from heating and cooling, we can estimate the impact of occupancy without any schedule data. A standard frequentist analysis is compared with Bayesian inference, and the according regression formulas are listed in tables that are easy to implement into building performance simulations (BPS). Monthly as well as seasonal correlations are addressed, showing the critical importance of occupancy. A simple method, grounded on the power drain measurements aimed at generating boundary conditions for the BPS, is also introduced; it shows how, for this type of data and number of occupants, no more complexities are needed in order to obtain reliable predictions. For an average year, we overestimate the measured cumulative consumption by only 4.7%. The model can be easily generalised to a variety of datasets.

scholarly journals Life Cycle Assessment of an Office Building Based on Site-Specific Data

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2588 ◽  
Author(s):  
Peter Ylmén ◽  
Diego Peñaloza ◽  
Kristina Mjörnell
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Energy Use ◽  
Office Building ◽  
Impact Categories ◽  
Time Data ◽  
Specific Data ◽  
Environmental Impact Categories ◽  
The Impact

Life cycle assessment (LCA) is an established method to assess the various environmental impacts associated with all the stages of a building. The goal of this project was to calculate the environmental releases for a whole office building and investigate the contribution in terms of environmental impact for different parts of the building, as well as the impact from different stages of the life cycle. The construction process was followed up during production and the contractors provided real-time data on the input required in terms of building products, transport, machinery, energy use, etc. The results are presented for five environmental impact categories and, as expected, materials that constitute the main mass of the building and the energy used during operation contribute the largest share of environmental impact. It is usually difficult to evaluate the environmental impact of the materials in technical installations due to the lack of data. However, in this study, the data were provided by the contractors directly involved in the construction and can, therefore, be considered highly reliable. The results show that materials for installations have a significant environmental impact for four of the environmental impact categories studied, which is a noteworthy finding.

scholarly journals Life cycle assessment of low-rise office building with different structure–envelope configurations

2016 ◽  
Vol 43 (3) ◽  
pp. 193-200 ◽  
Author(s):  
Othman Subhi Alshamrani
Keyword(s):  
New York ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Energy Use ◽  
Precast Concrete ◽  
Market Price ◽  
Steel Structures ◽  
Office Building ◽  
The Impact

This paper presents life cycle assessment approach to study the impact of structure and envelope types on the energy consumption and environmental impact of an office building in New York City, USA. In addition, the future costs of environmental impact for various structure and envelope options are assessed according to the current practice and market price of CO2. Eight structure and envelope types for the low-rise office building are investigated, which include concrete and steel structures with various combinations of envelopes. The parameters such as life cycle energy use, global warming potential, and water, air and land emissions are analyzed. The energy simulation is performed by using eQUEST software while the environmental impact is assessed by using ATHENA® impact estimator. The building with concrete frame is proved to have lower environmental impact compared to that with steel frame. The precast concrete building is found to be the most economical alternative due to the minimal environmental impact cost. This study is expected to enable the decision makers and building owners to select the best alternative with respect to energy use, and environmental and economic constraints.

scholarly journals Data-driven analysis of electricity use for office buildings: a Norwegian case study

2021 ◽  
Vol 246 ◽  
pp. 04005
Author(s):  
Yuemin Ding ◽  
Yiyu Ding ◽  
Natasa Nord
Keyword(s):  
Energy Use ◽  
Polynomial Regression ◽  
Primary Energy ◽  
Office Building ◽  
Data Driven ◽  
Monitoring And Control ◽  
Improve Energy Efficiency ◽  
Electricity Use ◽  
And Control ◽  
The Impact

Buildings are major consumers of primary energy and main contributors to carbon emission. To improve energy efficiency, it is essential to understand the characteristics of energy use in buildings. This study uses an in-use office building with digital systems for monitoring and control in Trondheim, Norway, as the study case. Based on data collected from this office building, a data-driven analysis was conducted to capture the characteristics of electricity use of different parts in the office building. The approaches used in this study included statistical analysis and polynomial regression. The impact of occupancy level on the total electricity use, the electricity use in office areas, and that in corridors & meeting rooms was also studied. The hourly electricity use profiles were obtained for ventilation fans and the cantina. In the end, the electricity use characteristics and existing issues in this office building were discussed.

scholarly journals Impact of office tenants’ electricity use on the cost-optimal solutions in a cold climate

2021 ◽  
Vol 246 ◽  
pp. 05001
Author(s):  
Helena Kuivjõgi ◽  
Liina Laas ◽  
Andrea Ferrantelli ◽  
Martin Thalfeldt
Keyword(s):  
Input Data ◽  
Energy Use ◽  
Energy Performance ◽  
Cold Climate ◽  
Office Building ◽  
Optimal Solutions ◽  
Pv System ◽  
Electricity Use ◽  
The Cost ◽  
The Impact

The buildings’ energy performance requirements in Estonia are based on cost-optimality analysis according to the EPBD and pre-defined building performance simulation (BPS) input data from EN 16798-1:2019. Previous studies have shown that the real electricity use of office building tenants differs from the currently used input data in BPS in Estonia – less in total energy use, but more in the shape of the profiles. The aim of this work is to investigate what is the impact of these differences on the cost-optimal solutions, which are identified based on BPS and the self-consumption of the photovoltaic panel (PV) systems. This study describes the energy performance and construction cost analysis of a new office building in Tallinn, Estonia. BPS based on the EN 16798-1 and a model derived from measurements in a real building were conducted and cost-optimal building solutions identified. The variables were building envelope insulation thickness, air handling unit size and effectiveness, electrical lighting control principles and PV system nominal power. The calculated energy use of the building with the two different sets of input data differed significantly. However, the set of cost-optimal solutions identified with EN 16798-1:2019 input data had minor differences from the set of solutions identified with the more realistic model. The decrease of net present value over 20-year period for cost-optimal solution was 11-14 €/m2 compared to the designed building.The realistic office tenants’ electricity model increased the calculated self-consumption of the PV system from 95% to 100%.

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.

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