scholarly journals Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE

2019 ◽  
Vol 8 (4) ◽  
pp. 163 ◽  
Author(s):  
Julian F. Rosser ◽  
Gavin Long ◽  
Sameh Zakhary ◽  
Doreen S. Boyd ◽  
Yong Mao ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Energy Demand ◽  
Energy Use ◽  
Housing Stock ◽  
Building Energy ◽  
Urban Housing ◽  
Energy Simulation ◽  
Spatially Explicit ◽  
Building Energy Simulation ◽  
Urban Scenes

Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

Building Energy Simulation with On-Site Weather Station

2016 ◽  
Vol 859 ◽  
pp. 88-92 ◽  
Author(s):  
Radu Manescu ◽  
Ioan Valentin Sita ◽  
Petru Dobra
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Weather Data ◽  
Building Energy Simulation ◽  
Existing Buildings ◽  
Existing Building ◽  
Simulation Tools ◽  
Heating And Cooling

Energy consumption awareness and reducing consumption are popular topics. Building energy consumption counts for almost a third of the global energy consumption and most of that is used for building heating and cooling. Building energy simulation tools are currently gaining attention and are used for optimizing the design for new and existing buildings. For O&M phase in existing buildings, the multiannual average weather data used in the simulation tools is not suitable for evaluating the performance of the building. In this study an existing building was modeled in EnergyPlus. Real on-site weather data was used for the dynamic simulation for the heating energy demand with the aim of comparing the measured energy consumption with the simulated one. The aim is to develop an early fault detection tool for building management.

Energy Impacts of Nonlinear Behavior of Phase Change Materials When Applied to Opaque Building Envelopes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Paulo Cesar Tabares-Velasco
Keyword(s):  
Phase Change ◽  
Energy Use ◽  
Phase Change Materials ◽  
Energy Savings ◽  
Peak Load ◽  
Building Energy ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Peak Reduction ◽  
The Impact

Research on phase change materials (PCM) as a potential technology to reduce peak loads and heating, ventilation and air conditioning (HVAC) energy use in buildings has been conducted for several decades, resulting in a great deal of literature on PCM properties, temperature, and peak reduction potential. However, there are few building energy simulation programs that include PCM modeling features, and very few of these have been validated. Additionally, there is no previous research that indicates the level of accuracy when modeling PCMs from a building energy simulation perspective. This study analyzes the effects a nonlinear enthalpy profile has on thermal performance and expected energy benefits for PCM-enhanced insulation. The impact of accurately modeling realistic, nonlinear enthalpy profiles for PCMs versus simpler profiles is analyzed based on peak load reduction and energy savings using the conduction finite difference (CondFD) algorithm in EnergyPlus. The PCM and CondFD models used in this study have been previously validated after intensive verification and validation done at the National Renewable Energy Laboratory. Overall, the results of this study show annual energy savings are not very sensitive to the linearization of enthalpy curve. However, hourly analysis shows that if simpler linear profiles are used, users should try to specify a melting range covering roughly 80% of the latent heat; otherwise, hourly results can differ by up to 20%.

scholarly journals Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2180
Author(s):  
Mehrdad Rabani ◽  
Habtamu Bayera Madessa ◽  
Natasa Nord
Keyword(s):  
Energy Use ◽  
Ventilation System ◽  
Building Energy ◽  
Building Envelope ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Visual Comfort ◽  
Indoor Climate ◽  
Building Energy Use ◽  
Air System

Simultaneous satisfaction of both thermal and visual comfort in buildings may be a challenging task. Therefore, this paper suggests a comprehensive framework for the building energy optimization process integrating computational fluid dynamics (CFD) daylight simulations. A building energy simulation tool, IDA Indoor Climate and Energy (IDA-ICE), was coupled with three open-source tools including GenOpt, OpenFOAM, and Radiance. In the optimization phase, several design variables i.e., building envelope properties, fenestration parameters, and Heating, Ventilation and Air-Conditioning (HVAC) system set points, were selected to minimize the total building energy use and simultaneously improve thermal and visual comfort. Two different scenarios were investigated for retrofitting of a generic office building located in Oslo, Norway. In the first scenario a constant air volume (CAV) ventilation system with a local radiator in each zone was used, while an all-air system equipped with a demand control ventilation (DCV) was applied in the second scenario. Findings showed that, compared to the reference design, significant reduction of total building energy use, around 77% and 79% in the first and second scenarios, was achieved respectively, and thermal and visual comfort conditions were also improved considerably. However, the overall thermal and visual comfort satisfactions were higher when all-air system was applied.

Energy Saving Opportunities Using Building Energy Simulation for a Typical Mosque in Kuwait

2010 ◽  
Author(s):  
Adnan Al Anzi ◽  
Basma Al-Shammeri
Keyword(s):  
Energy Conservation ◽  
Thermal Behavior ◽  
Life Cycle Cost ◽  
Energy Use ◽  
Simulation Analysis ◽  
Building Energy ◽  
Simulation Software ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
The State Of Kuwait

The weather conditions in Kuwait impose a difficult HVAC building operation due to the hot and arid climate. Most of the time, high ambient temperatures in Kuwait exceed 48° C, which result in difficult indoor comfort condition. Mosques are religious buildings with intermittent occupancy, due to their special cultural and religious requirements. In fact, prayers schedule is scattered throughout five daily times, with a maximum use around noon times on Fridays only. In addition, the number of mosques is increasing, due to population growth, and imposes high electrical load requirements on the public authorities in Kuwait. This paper demonstrates and analyzes thermal behavior of a typical mosque in the state of Kuwait. An energy audit is performed using state of the art building energy simulation software (Visual DOE 4.1). The simulation tool is intended to analyze the thermal behavior of the audited mosques and is used to asses potential energy conservation opportunities for future mosque design in Kuwait. Data collection including drawings, site visits and total daily kWh monitoring are performed to carry out the simulation analysis. It is found that an annual energy use savings up to 72% can be achieved through improvements of buildings envelope designs and operating strategies. In addition, life cycle cost LCC analysis is performed to make economical assessment of the energy conservation measures that are evaluated in this study. It was found that a LCC saving around 40% can be achieved with a simple payback period of less than 4 years.

Vision-based human activity recognition for reducing building energy demand

2021 ◽  
pp. 014362442110261
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Kaiser Calautit ◽  
Jo Darkwa ◽  
Christopher Wood
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Air Conditioning ◽  
Building Energy ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Learning Approach ◽  
Dynamic Changes ◽  
Air Conditioning Systems

Occupancy behaviour in buildings can impact the energy performance and the operation of heating, ventilation and air-conditioning systems. To ensure building operations become optimised, it is vital to develop solutions that can monitor the utilisation of indoor spaces and provide occupants’ actual thermal comfort requirements. This study presents the analysis of the application of a vision-based deep learning approach for human activity detection and recognition in buildings. A convolutional neural network was employed to enable the detection and classification of occupancy activities. The model was deployed to a camera that enabled real-time detections, giving an average detection accuracy of 98.65%. Data on the number of occupants performing each of the selected activities were collected, and deep learning–influenced profile was generated. Building energy simulation and various scenario-based cases were used to assess the impact of such an approach on the building energy demand and provide insights into how the proposed detection method can enable heating, ventilation and air-conditioning systems to respond to occupancy’s dynamic changes. Results indicated that the deep learning approach could reduce the over- or under-estimation of occupancy heat gains. It is envisioned that the approach can be coupled with heating, ventilation and air-conditioning controls to adjust the setpoint based on the building space’s actual requirements, which could provide more comfortable environments and minimise unnecessary building energy loads. Practical application Occupancy behaviour has been identified as an important issue impacting the energy demand of building and heating, ventilation and air-conditioning systems. This study proposes a vision-based deep learning approach to capture, detect and recognise in real-time the occupancy patterns and activities within an office space environment. Initial building energy simulation analysis of the application of such an approach within buildings was performed. The proposed approach is envisioned to enable heating, ventilation and air-conditioning systems to adapt and make a timely response based on occupancy’s dynamic changes. The results presented here show the practicality of such an approach that could be integrated with heating, ventilation and air-conditioning systems for various building spaces and environments.

Energy Impacts of Nonlinear Behavior of PCM When Applied Into Building Envelope

2012 ◽  
Author(s):  
Paulo Cesar Tabares-Velasco
Keyword(s):  
Energy Use ◽  
Phase Change Materials ◽  
Energy Savings ◽  
Peak Load ◽  
Building Energy ◽  
Building Envelope ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Peak Reduction ◽  
The Impact

Research on phase change materials (PCM) as a potential technology to reduce peak loads and HVAC energy use in buildings has been conducted for several decades, resulting in a great deal of literature on PCM properties, temperature, and peak reduction potential. However, there are few building energy simulation programs that include PCM modeling features, and very few of these have been validated. Additionally, there is no previous research that indicates the level of accuracy when modeling PCMs from a building energy simulation perspective. This study analyzes the effects a nonlinear enthalpy profile has on thermal performance and expected energy benefits for PCM-enhanced insulation. The impact of accurately modeling realistic, nonlinear enthalpy profiles for PCMs versus simpler profiles is analyzed based on peak load reduction and energy savings using the Conduction Finite Difference (CondFD) algorithm in EnergyPlus. The PCM and CondFD models used in this study have been previously validated after intensive verification and validation done at the National Renewable Energy Laboratory. Overall, the results of this study show annual energy savings are not very sensitive to the linearization of enthalpy curve. However, hourly analysis shows that if simpler linear profiles are used, users should try to specify a melting range covering roughly 80% of the latent heat, otherwise, hourly results can differ by up to 20%.

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