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 Building Energy an Simulation Model for Analyzing Energy Saving Options of Multi-Span Greenhouses

2020 ◽  
Vol 10 (19) ◽  
pp. 6884
Author(s):  
Adnan Rasheed ◽  
Cheul Soon Kwak ◽  
Hyeon Tae Kim ◽  
Hyun Woo Lee
Keyword(s):  
Energy Demand ◽  
Natural Ventilation ◽  
Thermal Environment ◽  
Dynamic Control ◽  
Weather Conditions ◽  
Building Energy ◽  
Design Parameters ◽  
Heating And Cooling ◽  
North Wall ◽  
Local Weather

This study proposes a multi-span greenhouse Building Energy Simulation (BES) model using a Transient System Simulation (TRNSYS)-18 program. A detailed BES model was developed and validated to simulate the thermal environment in the greenhouse under different design parameters for the multi-span greenhouse. Validation of the model was carried out by comparing the results from computed and experimental greenhouse internal temperatures. The statistical analyses produced an R2 value of 0.84, a root mean square error (RMSE) value of 1.8 °C, and a relative (r)RMSE value of 6.7%, showing good agreement between computed and experimental results. The validated proposed BES model was used to evaluate the effect of multi-span greenhouse design parameters including thermal screens, number of screens, orientation, covering materials, double glazing, north-wall insulation, roof geometry, and natural ventilation, on the annual energy demand of the greenhouse, subjected to Taean Gun (latitude 36.88° N, longitude 126.24° E), Chungcheongnam-do, South Korea winter and summer season weather conditions. Additionally, the proposed BES model is capable of evaluating multi-span greenhouse design parameters with daily and seasonal dynamic control of thermal and shading screens, natural ventilation, as well as heating and cooling set-points. The TRNSYS 18 program proved to be highly flexible for carrying out simulations under local weather conditions and user-defined design and control of the greenhouse. The statistical analysis of validated results should encourage the adoption of the proposed model when the underlying aim is to evaluate the design parameters of multi-span greenhouses considering local weather conditions and specific needs.

scholarly journals Development of a Building Energy Simulation Model for Control of Multi-Span Greenhouse Microclimate

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1236
Author(s):  
Adnan Rasheed ◽  
Cheul Soon Kwak ◽  
Wook Ho Na ◽  
Jong Won Lee ◽  
Hyeon Tae Kim ◽  
...  
Keyword(s):  
Simulation Model ◽  
Natural Ventilation ◽  
Winter Season ◽  
Weather Conditions ◽  
Building Energy ◽  
Energy Simulation ◽  
Building Energy Simulation ◽  
Proposed Model ◽  
Heating Loads ◽  
Maximum Heating

In this study, we propose a building energy simulation model of a multi-span greenhouse using a transient system simulation program to simulate greenhouse microenvironments. The proposed model allows daily and seasonal control of screens, roof vents, and heating setpoints according to crop needs. The proposed model was used to investigate the effect of different thermal screens, natural ventilation, and heating setpoint controls on annual and maximum heating loads of a greenhouse. The experiments and winter season weather conditions of greenhouses in Taean Gun (latitude 36.88° N, longitude 126.24° E, elevation 45 m) Chungcheongnam-do, South Korea was used for validation of our model. Nash–Sutcliffe efficiency coefficients of 0.87 and 0.71 showed good correlation between the computed and experimental results; thus, the proposed model is appropriate for performing greenhouse thermal simulations. The results showed that the heating loads of the triple-layered screen were 70% and 40% lower than that of the single-screen and double-screen greenhouses, respectively. Moreover, the maximum heating loads without a screen and for single-, double-, and the triple-layered screens were 0.65, 0.46, 0.41, and 0.34 MJ m−2, respectively. The analysis of different screens showed that Ph-77 (shading screen) combined with Ph-super (thermal screen) had the least heating requirements. The heating setpoint analysis predicted that using the designed day- and nighttime heating control setpoints can result in 3%, 15%, 14%, 15%, and 40% less heating load than when using the fixed value temperature control for November, December, January, February, and March, respectively.

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.

scholarly journals Buildingenergy.ninja: A web-based surrogate model for instant building energy time series for any climate.

2021 ◽  
Vol 2042 (1) ◽  
pp. 012012
Author(s):  
Paul Westermann ◽  
Guillaume Rousseau ◽  
Ralph Evins
Keyword(s):  
Time Series ◽  
Surrogate Model ◽  
Building Design ◽  
Building Energy ◽  
Surrogate Models ◽  
Energy Simulation ◽  
Series Data ◽  
Design Parameters ◽  
Building Energy Simulation ◽  
Building Performance

Abstract Machine learning-based surrogate models are trained on building energy simulation input and output data. Their key advantage is their computational speed allowing them to produce building performance estimates in fractions of a second. In this work we showcase the use of deep convolutional neural network surrogate models embedded into a web application, allowing users to rapidly explore building performance at high spatio-temporal resolution. Users can pick any climate on an interactive map, customize a building design with thirteen decisive design parameters, and the surrogate model allows them to retrieve hourly heating and cooling load time series data in fractions of a second. In this work, we further show that the surrogate model reaches an accuracy of R 2 > 0.93 (MAE < 0.27 kWh) for unseen design specifications and climates. These results motivate the use of computationally cheap surrogate models to replace building energy simulation for a wide variety of tasks in the future.

scholarly journals Impact of Natural Ventilation on the Thermal and Energy Performance of Buildings in a Mediterranean Climate

Buildings ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 123 ◽  
Author(s):  
A. Moret Rodrigues ◽  
Miguel Santos ◽  
M. Glória Gomes ◽  
Rogério Duarte
Keyword(s):  
Natural Ventilation ◽  
Mediterranean Climate ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Simulation ◽  
Wind Effect ◽  
Building Energy Simulation ◽  
Simulation Tool ◽  
Energy Performance Of Buildings ◽  
Ventilation Rates

Natural ventilation plays an important role on the thermal and energy performance of a building. The present study aims to analyze the natural ventilation conditions of a dwelling in a Mediterranean climate and their impacts on the thermal and energy performance using an advanced building energy simulation tool. Several multi-zone simulations were carried out. In the summer, the simulations were performed under free-floating conditions, whereas in the winter they were carried out under controlled temperature conditions. In the summer, ventilation scenarios with windows opened during certain periods of time and with or without permanent openings in the facades were analyzed. The existence of permanent openings proved to be an important factor of temperature control by lowering the average indoor zone temperatures during the day. Cross-ventilation also showed to be effective. In the winter, we simulated the existence or absence of permanent openings for room ventilation and their surface area. The results showed that the stack effect plays an important role in the ventilation and that in general it outperforms the wind effect. Sizing permanent openings according to the standard guidelines proved to be adequate in providing the expected ventilation rates on an average basis.

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 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.

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