Measured and predicted energy demand of a low energy building: important aspects when using Building Energy Simulation

2007 ◽  
Vol 28 (3) ◽  
pp. 223-235 ◽  
Author(s):  
F. Karlsson ◽  
P. Rohdin ◽  
M.-L. Persson
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Low Energy ◽  
Building Energy Simulation

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.

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.

scholarly journals Development and Optimization of a Building Energy Simulation Model to Study the Effect of Greenhouse Design Parameters

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2001 ◽  
Author(s):  
Adnan Rasheed ◽  
Jong Lee ◽  
Hyun Lee
Keyword(s):  
Energy Demand ◽  
Natural Ventilation ◽  
Thermal Environment ◽  
Local Environment ◽  
Building Energy ◽  
Energy Simulation ◽  
Design Parameters ◽  
Building Energy Simulation ◽  
Roof Shape ◽  
Systems Simulation

Energy management of the greenhouse is considered to be one of the most important challenges of greenhouse farming. Energy saving measures need considered, besides applying energy supplying techniques. To address this issue, a model was developed to simulate the thermal environment of a greenhouse using a Transient Systems Simulation Program (TRNSYS 17) as a building energy simulation (BES) platform. The model was calibrated by modifying the input parameters to minimize the uncertainties obtained from the results. Nash-Sutcliffe efficiency coefficients of 0.958 and 0.983 showed good agreement between the computed and experimental results. The proposed model was used to evaluate the effects of greenhouse design parameters, including roof shape, orientation, double-glazing, natural ventilation, coverings and their thickness, on its energy conservation capacity. It was found that the most suitable design for a greenhouse located in Daegu (latitude 35.53° N, longitude 128.36° E) South Korea would be east-west (E-W) oriented, with a gothic-shaped roof and double-glazing of PMMA (Polymethylmethacrylate) covering. Natural ventilation reduced the inside temperature of greenhouse, thereby reducing the energy demand of cooling. The model developed can help greenhouse farmers and researchers make pre-design decisions regarding greenhouse construction, taking their local environment and specific needs into consideration.

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.

scholarly journals Influence of Lightweight Aggregate Concrete Materials on Building Energy Performance

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 94
Author(s):  
Tara L. Cavalline ◽  
Jorge Gallegos ◽  
Reid W. Castrodale ◽  
Charles Freeman ◽  
Jerry Liner ◽  
...  
Keyword(s):  
Thermal Properties ◽  
Building Materials ◽  
Energy Savings ◽  
Lightweight Concrete ◽  
Lightweight Aggregate ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Building Energy Performance

Due to their porous nature, lightweight aggregates have been shown to exhibit thermal properties that are advantageous when used in building materials such as lightweight concrete, grout, mortar, and concrete masonry units. Limited data exist on the thermal properties of materials that incorporate lightweight aggregate where the pore system has not been altered, and very few studies have been performed to quantify the building energy performance of structures constructed using lightweight building materials in commonly utilized structural and building envelope components. In this study, several lightweight concrete and masonry building materials were tested to determine the thermal properties of the bulk materials, providing more accurate inputs to building energy simulation than have previously been used. These properties were used in EnergyPlus building energy simulation models for several types of commercial structures for which materials containing lightweight aggregates are an alternative commonly considered for economic and aesthetic reasons. In a simple model, use of sand lightweight concrete resulted in prediction of 15–17% heating energy savings and 10% cooling energy savings, while use of all lightweight concrete resulted in prediction of approximately 35–40% heating energy savings and 30% cooling energy savings. In more complex EnergyPlus reference models, results indicated superior thermal performance of lightweight aggregate building materials in 48 of 50 building energy simulations. Predicted energy savings for the five models ranged from 0.2% to 6.4%.

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