Energy consumption analysis of school buildings in Manitoba, Canada

Ouf, Mohamed; Issa, Mohamed

doi:10.1016/j.ijsbe.2017.05.003

Cited by 59 publications

(24 citation statements)

References 26 publications

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“…According to Natural Resources Canada (NRCan), the average annual energy consumption of schools in Canada is 472 kWh/m 2 [46]. School buildings in Ontario make up a 30% proportion of energy consumption in the public sector [47]. Another study by NRCan showed that after office and food storage buildings, as well as schools are the third largest energy consumer in Canada with 8% energy consumption among commercial and institutional buildings [48].…”

Section: Introductionmentioning

confidence: 99%

Effects of Extensive Green Roofs on Energy Performance of School Buildings in Four North American Climates

Mahmoodzadeh

Mukhopadhyaya

Valeo

2019

Water

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A comprehensive parametric analysis was conducted to evaluate the influence of the green roof design parameters on the thermal or energy performance of a secondary school building in four distinctively different climate zones in North America (i.e., Toronto, ON, Canada; Vancouver, BC, Canada; Las Vegas, NV, USA and Miami, FL, USA). Soil moisture content, soil thermal properties, leaf area index, plant height, leaf albedo, thermal insulation thickness and soil thickness were used as design variables. Optimal parameters of green roofs were found to be functionally related to meteorological conditions in each city. In terms of energy savings, the results showed that the light-weight substrate had better thermal performance for the uninsulated green roof. Additionally, the recommended soil thickness and leaf area index for all four cities were 15 cm and 5 respectively. The optimal plant height for the cooling dominated climates is 30 cm and for the heating dominated cities is 10 cm. The plant albedo had the least impact on the energy consumption while it was effective in mitigating the heat island effect. Finally, unlike the cooling load, which was largely influenced by the substrate and vegetation, the heating load was considerably affected by the thermal insulation instead of green roof design parameters.

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Section: Introductionmentioning

confidence: 99%

Effects of Extensive Green Roofs on Energy Performance of School Buildings in Four North American Climates

Mahmoodzadeh

Mukhopadhyaya

Valeo

2019

Water

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“…However, most of the work has been focused on understanding normal or "unsolicited" occupant behavior in built spaces (e.g., [36]). Studies highlight the "Intention-Behavior Gap", which occurs when occupants perform a specific action repeatedly based on habitual patterns rather than on a conscious cognitive intention to perform it [37][38][39][40]. A large body of knowledge highlights that feedback mechanisms can change OB in the immediate and medium and long term.…”

Section: Background and Theoretical Foundation Of The Workmentioning

confidence: 99%

Eyes on the Goal! Exploring Interactive Artistic Real-Time Energy Interfaces for Target-Specific Actions in the Built Environment

2021

Self Cite

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Current research is focused on sensing and modeling occupant behavior to predict it and automate building controls. Another line of research recommends influencing the behavior of occupants through feedback mechanisms and engagement. Yet, most of the work has focused on pushing occupants to reduce energy consumption over a long time and does not explore the potential to guide users to take specific actions promptly. The study examines a new interface mechanism that aims to solicit immediate and predefined actions from occupants. Building on seminal research in the field, the study uses art visualization to reinterpret social feedback. We test this approach in an immersive interaction space where participants react to artistic visuals to attain predefined settings for three indoor devices. In the 197 interactions recorded, participants’ overall actions conformed with the predefined goals. The participants were able to reach all or some of the targets in more than 80%, within an average of less than 30 seconds. We also see that complementing the visuals with textual hints improved the interaction in terms of engagement and accuracy. We conclude that ambient, abstract, and artistic real-time goal-driven feedback is effective in influencing immediate actions. We recommend that guiding occupants didactically has a strong potential for advancing building controls.

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“…Lately, an alternative tool for predicting consumption of energy was established by using a new type of tools, namely, artificial neural networks (ANNs), which evolved based on artificial intelligence and has high potential to be used in the construction field, as recent research demonstrated [18], and it is anticipated to have a greater performance compared to the traditional regression analysis [15]. A literature review suggested that neural networks have been intensively adopted in the development of forecasting energy consumption models (see, e.g., References [16,17,[19][20][21][22], as it can provide more accurate results compared with regression and simulation-based models [15].…”

Section: Application Of Neural Network (Nns) For Predicting Energy Cmentioning

confidence: 99%

“…Out of that, 82% was due to electric power consumption. Ouf and Issa [22] studied the consumption of energy in school's facility in Manitoba, Canada, for the last ten years. The study revealed that middle-aged schools had the highest rate of energy consumption.…”

Section: Prediction Of Energy Consumption In School Buildingsmentioning

confidence: 99%

Prediction of the Energy Consumption of School Buildings

Alshibani¹

2020

Applied Sciences

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The energy consumption of a constructed facility is a primary concern as a result of its impact on the total energy expenditure. It has been found that up to 70% of the power consumption in Saudi Arabia are caused by building structures and air conditioning (AC). Energy consumption in government-constructed buildings constitutes a considerable ≈13% of the consumption of the total energy in Saudi Arabia. Therefore, the government of Saudi Arabia initiated the Saudi Energy Efficiency Program (SEEP) that goals to lower the domestic energy severity by roughly 30% by 2030. This paper introduces a study carried out in Eastern Province in Saudi Arabia to identify factors influencing the consumption of energy in school facilities (which are built of concrete in hot and humid climate zones), investigate the correlation between those factors and their impacts on the consumption of energy in school facilities, and finally, develop a prediction model for the energy consumption of school facilities. The study was based on the utilization of 352 real-world datasets of energy consumption of operating schools across Eastern Province in Saudi Arabia. The developed energy prediction model considers eleven identified factors that influence the consumption of energy of constructed schools. The identified factors were utilized as input variables to build the model. A systematic search among different neural network (NN) design architectures was conducted to identify the optimal network model. Validation of the developed model on eight real-world cases demonstrated that the accuracy of the developed model was about 87.5%. Moreover, the findings of this study indicate that the weakest correlation between the input variables was recorded as −0.015 between “type of school” and “AC capacity,” while the strongest correlation was recorded as 0.95 between the variables of “number of classrooms” and “total air-conditioned area (sqm),” followed by “total air-conditioned area (sqm)” and “number of students,” which was recorded as 0.90. It is worth noting that “AC capacity” was the most significant predictor, which increased exponentially for high values of energy consumption, followed by “total school roof area.” The study also found that the age of the schools had a very small impact on energy consumption, although the age of the schools varied from 11 to 51 years. This was probably due to a good maintenance system applied by the Ministry of Education. The implication of the developed prediction model was that the model can be used by the Ministry of Education to predict the energy consumption and its associated cost for public school buildings for the purpose of budget allocation. The model may be utilized as a stand-alone application, or it can be integrated with an existing building information module (BIM)-based system.

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Energy consumption analysis of school buildings in Manitoba, Canada

Cited by 59 publications

References 26 publications

Effects of Extensive Green Roofs on Energy Performance of School Buildings in Four North American Climates

Effects of Extensive Green Roofs on Energy Performance of School Buildings in Four North American Climates

Eyes on the Goal! Exploring Interactive Artistic Real-Time Energy Interfaces for Target-Specific Actions in the Built Environment

Prediction of the Energy Consumption of School Buildings

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