scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

The impact of deep learning–based equipment usage detection on building energy demand estimation

2021 ◽  
pp. 014362442110347
Author(s):  
Shuangyu Wei ◽  
Paige Wenbin Tien ◽  
Yupeng Wu ◽  
John Kaiser Calautit
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Energy Demand ◽  
Internal Heat ◽  
Building Energy ◽  
Detection Accuracy ◽  
Equipment Usage ◽  
The Impact ◽  
Detection And Recognition

As external temperatures and internal gains from equipment rise, office buildings’ cooling demand and issues are likely to increase. Solutions such as demand-driven controls can help minimise energy consumption and maintain thermal comfort in buildings by coordinating the real-time heating, ventilation and air-conditioning (HVAC) use to the requirements of the conditioned spaces. The present study introduces a real-time equipment usage detection and recognition approach for demand-driven controls using a deep learning method. A Faster R-CNN model was trained and deployed to a camera. The performance of this model was assessed through different evaluation metrics. Based on the initial field experiment results, a detection accuracy of 76.21% was achieved. To investigate the impact of the proposed approach on building heating and cooling energy demand, the case study building was modelled and simulated. The results showed that the deep learning–based method predicted up to 35.95% lower internal heat gains compared to static or ‘fixed’ schedules based on the set conditions. Practical Application: As the appliances and equipment in building spaces contribute to the internal heat gains, their usage can influence the building energy demand and indoor thermal environment. Linking equipment usage with occupants’ presence in space may not be fully accurate and may lead to the over- or under-estimation of heat emissions, especially when the space is unoccupied, and the equipment is powered ON or the opposite. This approach can be integrated with demand-driven controls for HVAC systems, which can minimise unnecessary building energy consumption while maintaining a comfortable indoor environment using computer vision and deep learning detection and recognition methods.

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 ASSESSMENT OF ENERGY PERFORMANCE GAP OF AN OFFICE BUILDING

2021 ◽  
Vol 13 (0) ◽  
pp. 1-6
Author(s):  
Rasa Džiugaitė-Tumėnienė ◽  
Domas Madeikis
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Internal Heat ◽  
Energy Performance ◽  
Energy Model ◽  
Indoor Climate ◽  
Performance Gap ◽  
Protection Measures ◽  
Climate Parameters

The high share of global energy costs to create an indoor climate has been of increasing interest to the global community for several decades and is increasingly the focus of policy. This paper analyses the energy performance gap between actual energy consumption and energy demand obtained during the dynamic energy simulation and building certification. To identify the energy performance gap, an existing office of energy efficiency class B was selected as a case study. The simulation program IDA Indoor Climate and Energy was used to create a dynamic energy model, based on the designed documentation and the actual indoor climate parameters recorded by the building management system. The results of the case study showed that the accuracy and reliability of the results presented by the dynamic energy model of the building directly depend on the assumptions. The correct values of the internal heat gains, indoor climate parameters, human behavior, air quality levels at different times of the day and season, HVAC system operation parameters and operation modes, specific fan powers of ventilation systems, the seasonal energy efficiency of cooling equipment and characteristics of sun protection measures have to be selected.

scholarly journals Joint Analysis of Cost and Energy Savings for Preliminary Design Alternative Assessment

2020 ◽  
Vol 12 (18) ◽  
pp. 7507
Author(s):  
Carlo Iapige De Gaetani ◽  
Andrea Macchi ◽  
Pasquale Perri
Keyword(s):  
Energy Demand ◽  
Alternative Assessment ◽  
Energy Savings ◽  
Life Cycles ◽  
Decision Makers ◽  
Joint Analysis ◽  
Energy Demands ◽  
Design Alternatives ◽  
The Impact

The building sector plays a central role in addressing the problem of global energy consumption. Therefore, effective design measures need to be taken to ensure efficient usage and management of new structures. The challenging task for designers is to reduce energy demands while maintaining a high-quality indoor environment and low costs of construction and operations. This study proposes a methodological framework that enables decision-makers to resolve conflicts between energy demand and life cycle costs. A case study is analyzed to validate the proposed method, adopting different solutions for walls, roofs, floors, windows, window-to-wall ratios and geographical locations. Models are created on the basis of all the possible combinations between these elements, enriched by their thermal properties and construction/management costs. After the alternative models are defined, energy analyses are carried out for an estimation of consumption. By calculating the total cost of each model as the sum of construction, energy and maintenance costs, a joint analysis is carried out for variable life cycles. The obtained results from the proposed method confirm the importance of a preliminary assessment from both energy and cost points of view, and demonstrate the impact of considering different building life cycles on the choice of design alternatives.

scholarly journals EVALUATION OF ENERGY-EFFICIENT RETROFIT POTENTIAL FOR GOVERNMENT OFFICES IN INDIA

2021 ◽  
Vol 6 (2) ◽  
pp. 03-17
Author(s):  
Gazal Dandia ◽  
◽  
Pratheek Sudhakaran ◽  
Chaitali Basu ◽  
◽  
...  
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Efficient ◽  
Energy Demand ◽  
Energy Savings ◽  
High Energy ◽  
Payback Period ◽  
Total Energy Consumption ◽  
The Cost

Introduction: High energy consumption by buildings is a great threat to the environment and one of the major causes of climate change. With a population of 1.4 billion people and one of the fastest-growing economies in the world, India is extremely vital for the future of global energy markets. The energy demand for construction activities continues to rise and it is responsible for over one-third of global final energy consumption. Currently, buildings in India account for 35% of total energy consumption and the value is growing by 8% annually. Around 11% of total energy consumption are attributed to the commercial sector. Energy-efficient retrofitting of the built environments created in recent decades is a pressing urban challenge. Presently, most energy-efficient retrofit projects focus mainly on the engineering aspects. In this paper, we evaluate various retrofitting options, such as passive architectural interventions, active technological interventions, or a combination of both, to create the optimum result for the selected building. Methods: Based on a literature study and case examples, we identified various energy-efficient retrofit measures, and then examined and evaluated those as applied to the case study of Awas Bhawan (Rajasthan Housing Board Headquarters), Jaipur, India. For the evaluation, we developed a simulation model using EQuest for each energy measure and calculated the resultant energy savings. Then, based on the cost of implementation and the cost of energy saved, we calculated the payback period. Finally, an optimum retrofit solution was formulated with account for the payback period and ease of installation. Results and discussion: The detailed analysis of various energy-efficient retrofit measures as applied to the case study indicates that the most feasible options for retrofit resulting in optimum energy savings with short payback periods include passive architecture measures and equipment upgrades.

Real-time monitoring of occupancy activities and window opening within buildings using an integrated deep learning-based approach for reducing energy demand

2022 ◽  
Vol 308 ◽  
pp. 118336
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Kaiser Calautit ◽  
Jo Darkwa ◽  
Christopher Wood
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Real Time Monitoring ◽  
Window Opening ◽  
Reducing Energy

Assessment of energy and environmental performances of a bioclimatic dwelling in Algeria's North

2016 ◽  
Vol 38 (1) ◽  
pp. 64-88 ◽  
Author(s):  
N Belkacem ◽  
L Loukarfi ◽  
M Missoum ◽  
H Naji ◽  
A Khelil ◽  
...  
Keyword(s):  
Energy Balance ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Energy Savings ◽  
Heating System ◽  
Building Energy ◽  
Energy Performance ◽  
Solar Heating ◽  
Environmental Study ◽  
Building Energy Efficiency

Bioclimatic architecture strategies and solar active systems contribute strongly to the reduction of building energy demand and achieving thermal comfort for its occupants over the whole year. This paper deals with the study of the energy performance improvement of a pilot bioclimatic house located in Algiers (Algeria). First, a series of experimental measures are conducted during cold period to show the effect of passive and active solar gains on the improvement of the indoor air temperature of the house. Then, a dynamic model of a solar heating system coupled with a bioclimatic house has been developed using TRNSYS software and validated with experimental data. The validated model has been used to establish the energy balance of the pilot bioclimatic house without solar heating system and to compare them to those of a conventional house. Finally, the improvement of the energy balance of the pilot bioclimatic house has been done by passive and active ways. The passive one includes the increase of south facing windows size and the use of night cooling with the use of shading device in summer. The active one consists of the integration of a solar heating system. Furthermore, an environmental study has been performed. The experimental results show that the energy requirements of a pilot bioclimatic house are very low which is suitable for the use of solar heating system in building. The simulation results show that the application of bioclimatic strategies is a better way to provide thermal comfort in summer and decrease the space heating energy demand of the house with 48.70%. The active solar system will cover 67.74% of the energy demand for heating of the house. These energy savings generate a significant reduction in CO2 emissions. Practical application: This work will enable engineers and designers of modern buildings of buildings in a Mediterranean climate to improve building energy efficiency and reduce CO2 emissions by a conjunction of different passive heating and cooling techniques such as insulation, thermal mass, window shades, night ventilation, and the solar heating system. The paper provides designers an effective strategy in terms of energy savings and indoor thermal comfort while reducing CO2 emissions.

Retrofitting heritage office buildings in the UK: a case study

2018 ◽  
Vol 8 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Olubukola Tokede ◽  
Nilupa Udawatta ◽  
Mark Luther
Keyword(s):  
Energy Savings ◽  
Building Energy ◽  
Simulation Software ◽  
Office Buildings ◽  
Content Type ◽  
Heritage Buildings ◽  
Heritage Building ◽  
The Uk ◽  
Crucial Part

Purpose Heritage buildings are a crucial part of the UK built sector. They perpetuate a sense of identity, prestige and community. Many heritage buildings however tend to be energy inefficient and the scope for retrofitting such buildings is paramount. Heritage buildings require ratification from planning bodies in order to undertake any alteration on the building. This tends to create a bottleneck in the retrofitting of heritage office buildings. The paper aims to discuss this issue. Design/methodology/approach This study utilises a case study building in Scotland to evaluate the potential for retrofitting in a UK heritage office building. Building energy simulation software is used to generate the energy data in different retrofit options. A scenario analysis on the heritage status of the building is also undertaken. Findings The costs, energy consumption and carbon emission levels are evaluated and compared. It was found that the differential in annual energy savings achieved, based on the proportion of capital cost to operational cost, is 14.6 per cent in the heritage building, compared to 24.6 per cent in the non-heritage building. Originality/value The study suggests that government and other stakeholders should seek for ways of incentivising retrofit investments in heritage buildings. This will provide an effective way of minimising the contributions of the built environment to global warming and climate change.

Combined Solar and Internal Load Effects on Selection of Heat Reclaim-Economizer HVAC Systems

1990 ◽  
Vol 112 (2) ◽  
pp. 82-89
Author(s):  
Harry J. Sauer ◽  
Ronald H. Howell ◽  
Zijie Wang
Keyword(s):  
Heat Recovery ◽  
Energy Savings ◽  
Operating Time ◽  
Internal Heat ◽  
Hvac Systems ◽  
Load Level ◽  
Internal Load ◽  
Recovery System ◽  
Load Effects ◽  
Building Heat

The concern for energy conservation has led to the development and use of heat recovery systems which reclaim the building internal heat before it is discarded in the exhaust air. On the other hand, economizer cycles have been widely used for many years in a variety of types of HVAC systems. Economizer cycles are widely accepted as a means to reduce operating time for chilling equipment when cool outside air is available. It has been suggested that heat reclaim systems should not be used in conjunction with an HVAC system which incorporates an economizer cycle because the economizer operation would result in heat being exhausted which might have been recovered. Others suggest that the economizer cycle can be used economically in a heat recovery system if properly controlled to maintain an overall building heat balance. This study looks at potential energy savings of such combined systems with particular emphasis on the effects of the solar load (amount of glass) and the internal load level (lights, people, appliances, etc.). For systems without thermal storage, annual energy savings of up to 60 percent are predicted with the use of heat reclaim systems in conjunction with economizers when the heat reclaim has priority. These results strongly demonstrate the necessity of complete engineering evaluations if proper selection and operation of combined heat recovery and economizer cycles are to be obtained. This paper includes the basic methodology for making such evaluations.

scholarly journals Demand-side management strategies based on energy key perfomance indicators in real-time: Case study

2020 ◽  
Vol 10 (1) ◽  
pp. 5-16
Author(s):  
Sandra Milena Tellez- Gutierrez ◽  
Oscar German Duarte Velasco ◽  
Javier Rosero García
Keyword(s):  
Real Time ◽  
Performance Indicators ◽  
Energy Savings ◽  
New Technologies ◽  
Management Strategies ◽  
Demand Side Management ◽  
Key Performance Indicators ◽  
Demand Side ◽  
Advanced Metering

This paper sets out features of traditional Energy Key Performance Indicators (KPIs) employed in energy management programs; then, new indicators are proposed based on Advanced Metering Infrastructure (AMI) usage. These indicators make it possible to directly relate the amount of energy, type of end use and user consumption patterns. Analysis of AMI system information enables planning for differentiated Demand-Side Management (DSM) strategies. A case study developed at Universidad Nacional de Colombia - Bogotá campus is presented, which proposes new Energy Key Performance Indicators in Real Time. These indicators enable information analysis and DSM strategies that are appropriate for new technologies and that are aimed at increasing energy efficiency. Additionally, this paper presents the factors that have to be taken into account when implementing KPIs (Key Performance Indicators) and the decision-making process. This results in variable overall energy savings between 5 and 40%, according to the DSM strategy implemented.

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