Search

• Key operational parameters of 14 office buildings were reviewed. • Over 60% of the air handling units were not turned off outside normal occupied hours. • Most of the reviewed buildings did not have an economizer cycle program. • A sensitivity analysis was conducted with the reviewed operational parameters. • A mixed-integer genetic algorithm was applied to identify the optimal operational parameters. Operator decisions regarding daily and seasonal scheduling of systems' availability and setpoints play an important role in building performance. This paper reviews the key operational parameters of 14 office buildings in Ottawa, Canada. The results revealed that over 60% of the air handling units (AHUs) were not turned off outside of normal occupied hours, and most of them did not have an economizer cycle. The indoor temperature setpoints were between 20 and 24 °C. Based on these insights, a building performance simulation (BPS)-based sensitivity analysis was conducted to better understand the energy and comfort performance implications of common operator decisions. Eight operational parameters were studied: AHU start and stop times, seasonal switchover to heating and cooling times, heating and cooling season temperature setpoints, and ventilation rate and mode (i.e., constant or occupancy-based). The results revealed that the AHU start and stop times and the ventilation rate are the most critical operational parameters examined in terms of affecting the energy and comfort performance of the buildings investigated. Subsequently, a mixed-integer genetic algorithm was applied to identify the optimal operational parameters among the set of eight operational parameters investigated for four different heating dominated climate zones, nine different occupancy and three different envelope scenarios. The relationships between the variables of these scenarios and the optimal operational parameters were examined. [ABSTRACT FROM AUTHOR]

This paper is the second part of a two-part study on the performance of ground-source heat pumps (GSHPs) in single-family detached houses in Canada. In Part I the cooling performance of two GSHPs was tested, analyzed and evaluated over one cooling season. In this paper, the heating performance of three vertical closed-loop water-to-air GSHP systems installed in three new single-family detached houses in Innisfil (80 km north of Toronto, Canada) is monitored, analyzed and evaluated over one heating season. Results show that GSHP A , GSHP B and GSHP C were able to meet the peak heating load without using the auxiliary heating element. The seasonal coefficient of performance of heat pump of GSHP A , GSHP B and GSHP C is 4.2, 4.0 and 3.4, respectively, whereas the seasonal coefficient of performance of the GSHP system of GSHP A , GSHP B and GSHP C is 3.8, 3.4 and 2.7, respectively. GSHP A has the highest COP mainly because it is equipped with a variable speed compressor while GSHP B and GSHP C are equipped with two-stage compressors. The average temperature of water leaving the ground loop of GSHP A , GSHP B and GSHP C is 3.9 °C, 3.1 °C and 2.7 °C, respectively, where the average temperature of water leaving the ground loop varied by up to 6.3 °C throughout the heating season. The total power consumption and operating cost of GSHP A , GSHP B and GSHP C are less than conventional air-source heat pumps by up to 46%, 40% and 24%, respectively. It is concluded that GSHPs operate at high COPs without operational problems even in very cold climates if designed, sized and installed correctly. [ABSTRACT FROM AUTHOR]

In this paper, gray-box models of a residential HVAC system commonly found in Canadian households are developed. The system comprises of four mjor subsystems called heat recovery ventilator (HRV), air handling unit (AHU), air source heat pump (ASHP) and zone. Models were developed by writing energy balance equations of each subsystem. Measured data from residential HVAC system of Archetype Sustainable House located in Vaughan, Canada was used for parameter estimation and validation of each subystem model. The house is outfitted with temperature, flow rate and power consumption measurement sensors for all subsystems. Two distinct sets of parameeters were found using winter and summer datasets. Parameter estimation was carried out using pattern search with Latin hypercube method and Active-Set algorithm. Developed models were evaluated against measured data using several performance comparison metrics and showed a very high value of goodness of fit in the range of 70% and 88% during both winter and summer seasons. [ABSTRACT FROM AUTHOR]

• GHG estimates more sensitive to alternative water heater than space heating system. • HPWH increases lifetime GHGs in clean electricity grids when replacing EWH. • SAHPWH reduces GHGs more than HPWH in all climates. • Low difference in GHGs between HPWH and SAHPWH when space heating is not considered. Space and water heating contribute 62% and 21% of the residential greenhouse gas emissions (GHGs) in Canada, respectively, which corresponds to 11% of the GHGs within the country. Because both space and water heating are dominated by natural gas, the carbon footprint of Canada could be significantly reduced with a shift towards clean space and water heating, such as heat pump water heaters (HPWHs). HPWHs use electricity but are between two and three times more efficient than electric water heaters (EWHs). However, while they operate, HPWHs cool nearby ambient air which increases natural gas-dominant space heating loads. Solar assisted HPWHs (SAHPWHs) may allow the GHG reductions of HPWHs to be realized without increased space heating loads. The objective of this study was to assess the feasibility of HPWHs and SAHPWHs in terms of their GHG reductions across Canada and the United States. It was found that the systems reduced GHGs compared to EWHs and natural gas water heaters (NGWHs) in many locations, as indicated graphically within this paper. The locations and electricity grid mixes which have potential for the greatest GHG reductions are shown in this paper. [ABSTRACT FROM AUTHOR]

• A framework is developed to assess overheating in existing building in whole summer. • Calibration simulation model is used using field measurements of indoor temperature. • Overheating risk in existing school building is assessed. • Climate change effect is studied using current and future extreme RSWY years. • Effective passive mitigation measures are provided for future extreme climates. Canadian buildings have been primarily designed to withstand cold and long winters, not hot summers. With climate change and the increase in the intensity and severity of heatwaves, it has become important to investigate overheating in buildings. However, there are limited studies on assessing and mitigating the overheating risk in existing buildings that house vulnerable populations in cold climates, especially in Canada. This paper provides a framework for the systematic assessment of overheating risks and the development of passive mitigation strategies to reduce the overheating risks without increasing cooling energy consumption under current and future climates with simulation models that are calibrated based on measured indoor air temperatures and outdoor weather conditions. The framework is applied to a school building built in 1958. The calibrated building model achieved an RMSE of less than 0.6 °C compared with measurements, a Maximum-Absolute-Difference of less than 1.9 °C, and a 1 °C Percentage-Error of less than 10 %. The simulation results from the calibrated model predicted 110 overheating hours during the year 2020. The use of exterior blind roll or a combination of night cooling and other mitigation measures that reduce solar heat gain can achieve acceptable thermal conditions. In 2044 (the future extreme midterm year), night cooling with the exterior blind roll shading would be required during extreme heat events. Whereas during 2090 (the future long-term extreme year), additional mitigation measures such as a cool roof may be required to achieve an acceptable level of thermal conditions in the school. [ABSTRACT FROM AUTHOR]

Microgrids play an essential role in the integration of multiple distributed energy resources in buildings. They can meet critical loads in buildings while reducing peak loads and congestion and providing other types of ancillary services to the main electrical grid. Sizing the components of microgrids and scheduling their optimal operation while jointly integrating uncertain renewable energy generation and loads can significantly affect its ability to meet these objectives. In fact, microgrids face great challenges due to renewable energies and load uncertainties. In this paper, a two-stage stochastic programming model for optimal sizing and operation of various distributed energy resources for peak load shaving in district buildings is proposed. The first stage is related to the planning of photovoltaic panels and batteries, while the second stage aims to find the optimal operation of the system in grid-connected mode. The uncertainties are related to photovoltaic power generation and loads. The Generative Adversarial Network (GAN) is implemented to generate the scenarios of uncertain parameters, and the k-medoids classical method for scenario reduction is applied to decrease the number of scenarios. The proposed two-stage stochastic optimization is tested for a real case study of a university campus in Canada. [ABSTRACT FROM AUTHOR]

Typically the effect of thermal bridges on the energy performance of buildings is taken into account by implementing an average U-value in 1D whole building energy simulation program, which is referred to as the equivalent U-value method. This treatment accounts for the effect of thermal bridges on the overall thermal transmittance of envelope details, while their thermal inertia effect is ignored. In this paper, the dynamic effect of balcony slab as thermal bridges on the energy performance of a high-rise residential building is investigated by comparing the annual space conditioning loads obtained by the equivalent U-value method to that obtained from 3D dynamic modeling of the balcony junctions implemented in WUFI Plus program. The simulation results show that the equivalent U-value method underestimates the annual heating loads by 2.8–4.4% for the building as-designed. With the increase of balcony portion and better insulated walls, the difference in annual space heating loads modeled using these two methods is increased to 8.6–15.2%. With the implementation of balcony thermal break the annual heating loads are reduced by 8.8–25.7% and the difference between these two modeling methods is also reduced to 1.9–11.5% for the cases investigated. [ABSTRACT FROM AUTHOR]

The modeling and simulation of domestic hot water systems, including solar thermal and co-generation technologies, relies on accurate, high time-step resolution water consumption profiles. Due to the expensive nature of field studies, previous profiles have been limited in number of participants, or have been generated synthetically using probabilistic modelling techniques. The Solar City program in Halifax has measured hot water flow rate and temperature data in 119 homes at a time-step of 1 min; and includes a survey of occupancy rates and other relevant meta-data. In this paper, we discuss the data acquisition and processing (with a focus on flow rate data), and conduct analysis to reveal consumption trends and produce a new set of time-step profiles for modeling and simulation. Average hot water consumption per household is 172 L/day with a strong dependency on number of occupants. The hourly profile peaks twice daily, and shifts by several hours on Sunday. The average draw temperature was found to be 51.8 °C, which is lower than the default value of 55 °C applied by many building modeling/simulation packages. The average cold water supply temperature was found to be roughly sinusoidal about 12.4 °C with approximately one-month lag compared to ambient air temperature. [ABSTRACT FROM AUTHOR]

Vacuum insulation panels (VIPs) have thermal resistance values, at the center of the panel, up to 10 times or more than those of conventional thermal insulation materials. In Canada, known for its predominantly extreme cold climate, the potential to apply VIPs in the building construction industry is estimated to be enormous, particularly with the introduction of the new 2011 National Energy Code of Canada for Buildings (NECB 2011). VIPs can play a major role in Canadian buildings to meet energy requirements of the NECB 2011. However, the lack of long-term performance credentials and various constructability issues are among the major barriers for mass application of VIPs in the Canadian construction industry. This paper presents the design strategy, construction details, instrumentation and thermal performance monitoring observations of a VIP retrofitted wall system in Yukon, located in Northern Canada. Experience gained from the construction process and available thermal performance data over a period of three years provide encouraging indicators for the constructability and long-term thermal performance of VIPs in Canadian subarctic weather. [ABSTRACT FROM AUTHOR]

Through tests conducted at the Toronto and Region Conservation Authority's (TRCA) Archetype Sustainable Houses, a hybrid residential space heating system with a supervisory controller was monitored and studied to evaluate its performance and effectiveness for the heating season. A high efficiency natural gas furnace (NGF) and an electric air source heat pump (ASHP) were coupled together to meet the space heating demand of the house. This integrated system is called the cloud based Smart Dual Fuel Switching System (SDFSS) that considers time-of-use (TOU) pricing, fuel cost, short-term weather forecast, and equipment efficiencies and capacities. This multi-variable decision-making process defines an optimal schedule for the hybrid system to run more cost effectively. This paper analyses two separate SDFSSs. According to these analyses, the SDFSS systems showed lower operating cost with respect to the furnace or the ASHP system alone with various carbon tax (CT) levels from $0 to $250/tonne of CT with an increment of $10/tonne of CT that were simulated, along with a significant GHG emission reduction relative to the conventional heating systems. Furthermore, with these technologies, Canada's residential sector could potentially meet Canada's Paris Agreement goals. The SDFSS technology is a is flexible, user friendly, ubiquitous technology for the smooth transition from today's natural gas dominated space heating system to the future's low carbon infrastructure powered by a heat pump and renewable energy. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents results from a field trial with a hybrid micro-cogeneration system applied to satisfy both thermal and power needs of a typical Canadian detached house. The hybrid system consists of an internal combustion engine integrated with a high efficiency furnace. It was installed, integrated with the rest of the mechanical systems and demonstrated at the Canadian Center for Housing Technology (CCHT). Cost analysis was performed to examine the economics of such systems in relation to different price structures imposed by the electric utilities. The study revealed that the hybrid micro-cogeneration system performed reliably and generated both heat and power with high efficiency during the heating season and was able to satisfy the heating demand under very cold weather conditions. The power generated by the system was first directed to the house satisfying 50% of the electric load with the other 50% being exported to the grid. The project demonstrated that micro-cogeneration systems when sized to meet the base electric load and design thermal load of the residence are a valuable alternative to the central power generation plants and are able to reliably satisfy the house thermal/power demand even under extreme cold conditions. [Copyright &y& Elsevier]

Abstract: The residential sector uses nearly 30% of all electricity in Canada, and, it is important to know how this energy is being used, so that savings may be identified and realised. We propose a method that can be applied to hourly whole-house electrical energy data to partially disaggregate total household electricity use into five load categories/parameters (base load, activity load, heating season gradient, cooling season gradient and lowest external temperature at which air-conditioning is used). This paper develops the proposed method, and verifies it using high-resolution end-use data from twelve households with known characteristics. We then apply the method to hourly whole-house (smart meter) data from 327 households in Ontario. The roll-out of smart (advanced) metering infrastructure in many countries will make hourly whole-house data abundant, and we propose that this method could be widely applied by utilities to target their demand-side management programs towards households more likely to provide benefits, thus increasing the cost-effectiveness of such programs. [Copyright &y& Elsevier]

Abstract: Private and public sectors own and operate an array of office buildings that consume energy and contribute to the emission of greenhouse gases (GHG). Energy demands can be reduced by applying energy retrofit measures (ERMs) to existing buildings. The choice of ERMs involves evaluation of applicability, energy end uses and cost of application versus energy savings. This paper describes a methodology developed to screen office buildings for their current level of energy consumption and potential for retrofit application. Selection of an optimal set of ERMs is influenced by climate, occupancy, heating and cooling systems, envelope properties and building geometry. When assessing the implications of applying ERMs to a large building stock it is vital to screen the complete building set for optimal retrofit opportunities. This can be accomplished by characterizing office building stock into a manageable set of archetypes and simulating building operation using energy simulation software. Using regression analyses, a model was developed for estimating the energy consumption. Present value analysis was used to optimize the evaluation of the various ERMs. The methodology developed can be used to simplify the ranking of buildings for retrofit; to select and combine ERMs, and to plan energy and GHG reduction activities. [ABSTRACT FROM AUTHOR]

Standby electricity use, or leaking electricity, is the electrical energy consumed by electrical appliances when they are turned off, or not in use. This electrical energy is used by internal or external transformers, or features such as remote control, memory, clock display, and instant-on, and represents a significant component of residential electricity consumption. In this paper, results from detailed field measurements of the standby power requirements of new stock and existing minor and miscellaneous household appliances are presented. Measurements were made in four electronic appliance retailers and 75 houses in Halifax, Canada, and cover a wide range of appliances. Using the findings from these field measurements and self-reported usage, the annual average standby energy consumption per household in the sample was estimated to be 427 kWh, which is equivalent to a constant load of 49 W. This could be reduced by 59% (to 177 kWh) if the standby power requirement of all appliances with a standby power requirement over 1 W was reduced to 1 W. The estimated household standby power consumption of 427 kWh per year is likely to be slightly lower than the actual, since major and hard-wired appliances are not included in the analysis. Future work on the measurement of standby power requirements of such appliances is needed to further understand the residential standby power consumption. [Copyright &y& Elsevier]

This paper investigates the application of surrogate modelling to create bottom-up archetype models, to improve the energy performance of specific residential building archetypes. The objective of this study is to quantify the application range(s) of surrogate models to predict and improve the annual energy performance of single-family home archetypes. Six distinct surrogate models were developed using multivariate regression and artificial neural networks, based on 6000 simulated samples extracted from four different century home archetypes in Toronto (Canada) as a result of random field study within a target neighbourhood. The results show that a single surrogate model can predict the annual energy requirements for four distinct archetypes with 7.87% average error with a multivariate regression model and 4.13% average error using an artificial neural network in 0.00047 and 0.013 ms respectively. These results indicate that the bottom-up surrogate model can describe the relationship between retrofit strategies and annual energy demand with substantial computational time savings. The findings of this study support the future application of surrogate modelling to develop bottom-up archetype models to continue the development of the Toronto Archetype Project and to support a national retrofit strategy in Canada. [ABSTRACT FROM AUTHOR]

With the present leaning towards well-insulated and airtight energy-efficient buildings and houses, air-to-air heat/energy exchangers have become very important for maintaining a good indoor air quality (IAQ) via continuous mechanical ventilation along with decreasing energy needed to condition the inbound fresh air. The present study aims to experimentally evaluate the performances of a dual-core ERV-HRV system in terms of heat and energy recovery in residential environment under Atlantic Canada weather. An IoT platform was designed and integrated to this ventilation system in order to collect and log real-time data. Moreover, this paper presents a detailed mathematical model of the proposed system to predict its energy exchange efficiency, using the effectiveness-NTU (i.e. ε - N T U) approach. The model was subsequently validated against experimental measurements within laboratory environment. As proved by experimental data, the theoretical model can predict, with a relative discrepancy less than 10%, the system energy exchange efficiency. Results from a real house located at Sainte-Anne-de-Kent in New-Brunswick, Canada are presented in this study, and the system efficiency was assessed under hot and cold weather conditions. The research findings show that, in the winter, the measured sensible and latent efficiencies are in ranges of 65.66–85.05% and 38.46–58.96%, respectively. Whereas, for the summer season, the corresponding effectiveness ranged from 31.7% to 85.72%, and from 68.51% to 92.69%, respectively. As consequence, the experimental results analysis revealed that the proposed dual-core exchanger is efficient during both heating and cooling seasons. [ABSTRACT FROM AUTHOR]

• A retrofit selection strategy employing economic and environmental decision criteria was proposed. • Building energy retrofits were evaluated through a life cycle thinking approach. • Retrofits with operational emissions savings may not be beneficial when considering life cycle impacts. • Energy mix and the heating system of the house greatly impact the effectiveness. • Retrofit selection changes with stakeholder priorities on environment and economics. Improving the energy performance of buildings has been a much-discussed topic over the past few decades. With the current focus on climate change mitigation, emissions reduction has also come to the forefront of this discussion. Retrofitting is an option to improve the energy and emissions performance of buildings. However, in the residential building stock, retrofit planning for existing buildings faces many complexities due to variations in climatic conditions and macro-environment as well as the presence of multiple stakeholder groups. Thus, in identifying solutions, it is necessary to take a holistic perspective that covers these different dimensions. In this paper, commonly used building energy retrofits were evaluated through a life cycle thinking approach. The performance of various retrofit options applicable for single family detached housing were evaluated using the HOT2000 energy simulation software package under the varying climatic conditions and energy supply scenarios across Canada. The retrofits were evaluated in terms of the additional investment, energy use and cost reduction achieved over the life cycle, and life cycle emissions reduction. The findings indicate that the provincial energy mix and the heating system of the house (i.e. electric or natural gas) play a major role in determining the effectiveness of a retrofit, and this "effectiveness" changes at different stakeholder levels. However, not all retrofits that reduce emissions make economic sense and vice versa when life cycle thinking comes into play. The findings will be useful for building owners and occupants as well as for policy developers and other decision makers interested in demand side management. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the development and implementation of Model Predictive Control (MPC) in institutional buildings. This methodology relies on Artificial Intelligence (AI) for model development. An appropriate control-oriented model is a critical component in MPC; model development is no easy task, and it often requires significant technical expertise, effort and time, along with a substantial amount of information. AI techniques enable rapid development and calibration of models using a limited amount of information (i.e. measurements of few variables) while achieving relatively high accuracy. In this study, the MPC algorithm targets the reduction of natural gas consumption by optimizing the transition between night set-back and daytime indoor air set-point values as a function of the expected weather. This MPC strategy was implemented in an institutional building in Varennes (QC), Canada, during the heating season 2018–19. A significantly better performance was achieved when compared with "business as usual" control strategies: the natural gas consumption and greenhouse gas (GHG) emissions were reduced by approximately 22%, and the building heating demand by 4.3%. The proposed strategy is scalable and can be replicated in other buildings. [ABSTRACT FROM AUTHOR]