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 Influence of User-Related Parameters on Calculated Energy Use in Low-Energy School Buildings

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2985 ◽  
Author(s):  
Branko Simanic ◽  
Birgitta Nordquist ◽  
Hans Bagge ◽  
Dennis Johansson
Keyword(s):  
Energy Use ◽  
Ventilation System ◽  
Building Energy ◽  
Energy Performance ◽  
Hot Water ◽  
Low Energy ◽  
Building Energy Use ◽  
Low Energy Buildings ◽  
Demand Controlled Ventilation ◽  
The Impact

Literature and experience show that there are large discrepancies between the calculated and measured building energy usages, where user-related parameters are significant factors with regard to energy use in low-energy buildings. Furthermore, the difficulties encountered when quantifying these parameters compound these discrepancies. The main aim of this study was to provide feedback that would help the building industry and research communities to predict more accurately the impact of the user-related parameters on energy performance. The results of the study would, subsequently, contribute to minimizing the discrepancies between calculated and measured energy use. This article analyses simulated building energy use based on randomly chosen combinations of measured user-related parameters in three recently built low-energy schools in Sweden. The results show that energy performance can span from 30 to 160 kWh/(m² y) simply by varying the combination of previously measured user-related parameters in building energy simulations. The study shows that the set points for indoor air temperatures during the heating season and the energy required to run a demand-controlled ventilation system have an extensive influence, while tenant electricity use has a slightly lower influence on building energy use. Variations in occupancy rates and energy for hot water usage have the smallest influences on building energy use.

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 flEECe, an energy use and occupant behavior dataset for net-zero energy affordable senior residential buildings

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Frederick Paige ◽  
Philip Agee ◽  
Farrokh Jazizadeh
Keyword(s):  
Affordable Housing ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Hot Water ◽  
Occupant Behavior ◽  
Zero Energy ◽  
Net Zero Energy ◽  
Net Zero ◽  
Building Energy Use

AbstractThe behaviors of building occupants have continued to perplex scholars for years in our attempts to develop models for energy efficient housing. Building simulations, project delivery approaches, policies, and more have fell short of their optimistic goals due to the complexity of human behavior. As a part of a multiphase longitudinal affordable housing study, this dataset represents energy and occupant behavior attributes for 6 affordable housing units over nine months in Virginia, USA which are not performing to the net-zero energy standard they were designed for. This dataset provides researchers the ability to analyze the following variables: energy performance, occupant behaviors, energy literacy, and ecological perceptions. Energy data is provided at a 1 Hz sampling rate for four circuits: main, hot water heater, dryer, and HVAC. Building specifications, occupancy, weather data, and neighboring building energy use data are provided to add depth to the dataset. This dataset can be used to update building energy use models, predictive maintenance, policy frameworks, construction risk models, economic models, and more.

scholarly journals Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

Buildings ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 139 ◽  
Author(s):  
Rezvan Mohammadiziazi ◽  
Melissa M. Bilec
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Energy Use ◽  
Regression Tree ◽  
Building Energy ◽  
Gradient Boosting ◽  
Change Models ◽  
Building Energy Use ◽  
Extreme Gradient Boosting ◽  
Single Regression

Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale.

scholarly journals Framework for Developing a Low-Carbon Energy Demand in Residential Buildings Using Community-Government Partnership: An Application in Saudi Arabia

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4954
Author(s):  
Mohammad AlHashmi ◽  
Gyan Chhipi-Shrestha ◽  
Kh Md. Nahiduzzaman ◽  
Kasun Hewage ◽  
Rehan Sadiq
Keyword(s):  
Saudi Arabia ◽  
Energy Demand ◽  
Energy Use ◽  
Residential Buildings ◽  
Ghg Emissions ◽  
Clean Energy ◽  
Building Energy ◽  
Energy Performance ◽  
Low Carbon ◽  
Geographical Regions

Rapid population growth has led to significant demand for residential buildings around the world. Consequently, there is a growing energy demand associated with increased greenhouse gas (GHG) emissions. The residential building energy demand in arid countries such as Saudi Arabia is supplied with fossil fuel. The existing consumption pattern of fossil fuels in Saudi Arabia is less sustainable due to the depletion of fossil fuel resources and resulting environmental impacts. Buildings built in hot and arid climatic conditions demand high energy for creating habitable indoor environments. Enormous energy is required to maintain a cool temperature in hot regions. Moreover, climate change may have different impacts on hot climatic regions and affect building energy use differently. This means that different building interventions may be required to improve the performance of building energy performance in these geographical regions, thereby reducing the emissions of GHGs. In this study, this framework has been applied to Saudi Arabia, a hot and arid country. This research proposes a community–government partnership framework for developing low-carbon energy in residential buildings. This study focuses on both the operational energy demand and a cost-benefit analysis of energy use in the selected geographical regions for the next 30 years (i.e., 2050). The proposed framework primarily consists of four stages: (1) data collection on energy use (2020 to 2050); (2) setting a GHG emissions reduction target; (3) a building intervention approach by the community by considering cost, energy, and GHG emissions using the Technique for Order of Performance by Similarity to the Ideal Solution (TOPSIS) to select the best combinations in each geographical region conducting 180 simulations; and (4) a clean energy approach by the government using grey relational analysis (GRA) to select the best clean energy system on the grid. The clean energy approach selected six different renewable power generation systems (i.e., PV array, wind turbine, hybrid system) with two storage systems (i.e., battery bank and a combination of electrolyte, fuel cell, and hydrogen tank storage). This approach is designed to identify the best clean energy systems in five geographical regions with thirty scenario analyses to define renewable energy-economy benefits. This framework informs through many engineering tools such as residential building energy analysis, renewable energy analysis, multi-criteria decision analysis (MCDA) techniques, and cost-benefit analysis. Integration between these engineering tools with the set of energy policies and public initiatives is designed to achieve further directives in the effort to reach greater efficiency while downsizing residential energy demands. The results of this paper propose that a certain level of cooperation is required between the community and the government in terms of financial investments and the best combinations of retrofits and clean energy measures. Thus, retrofits and clean energy measures can help save carbon emissions (enhancing the energy performance of buildings) and decrease associated GHG emissions, which can help policy makers to achieve low-carbon emission communities.

scholarly journals Building energy use compilation and analysis (BECA). Part B: Retrofit of existing North American residential buildings

1983 ◽  
Vol 5 (3) ◽  
pp. 151-170 ◽  
Author(s):  
Leonard W. Wall ◽  
Charles A. Goldman ◽  
Arthur H. Rosenfeld ◽  
Gautam S. Dutt
Keyword(s):  
North American ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Building Energy Use

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 Big-data for building energy performance: Lessons from assembling a very large national database of building energy use

2015 ◽  
Vol 140 ◽  
pp. 85-93 ◽  
Author(s):  
Paul A. Mathew ◽  
Laurel N. Dunn ◽  
Michael D. Sohn ◽  
Andrea Mercado ◽  
Claudine Custudio ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Use ◽  
Building Energy ◽  
Energy Performance ◽  
National Database ◽  
Building Energy Performance ◽  
Building Energy Use ◽  
Large National Database

Impact of Shape on Building Energy Use in Tunisia

Solar Energy ◽  
2006 ◽  
Author(s):  
Kais Ouertani ◽  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Residential Buildings ◽  
Energy Performance ◽  
Analysis Tool ◽  
Architectural Form ◽  
Relative Compactness ◽  
Building Shape ◽  
Heating And Cooling ◽  
Building Form ◽  
The Impact

This paper investigates the impact of the architectural form on the energy performance of residential buildings in Tunisia. A relative compactness is defined as one indicator of a building shape. The results of the analysis indicate that a significant decrease in heating and cooling energy requirements can be obtained by minimizing the relative compactness of detached residential houses. A simplified analysis tool, suitable for early design process, is developed to assess the impact of building form on its energy performance for several cities in Tunisia.

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