• Energy Research
• 11. Sustainability close
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Abstract In China, more than 100 million people live in earth-based dwellings currently. However, there have been few studies on measures for improving indoor thermal environment and decreasing energy consumption of adobe buildings systematically. So, this paper studied the measures mentioned above. Firstly, parameters such as indoor thermal environment, PMV, thermal properties and thermodynamic disfigurement of building envelope were measured and analyzed on a quadrangle adobe dwelling in Gansu, China. Then, three optimization schemes (transforming layout only, transforming external walls only and changing the two simultaneously) were put forward. Finally, the thermal environment and energy consumption of the optimization schemes were analyzed by numerical simulation, and the cost benefit analysis of them was carried out. Among those schemes, scheme of changing layout and external walls simultaneously leads to the best performance in both the two respects mentioned above, while scheme of transforming layout only gives the worst performance. But, when both taking consideration of economy and energy saving, the preferred plan is scheme of changing layout only, and the worst is scheme of transforming layout and external walls simultaneously. This study has been conducted to give guidance when constructing or reconstructing quadrangle adobe dwelling, which is distinctive and historical in China.

Smart thermostats are one of the most prevalent home automation products. They learn occupant preferences and schedules, and utilize an accurate thermal model to reduce the energy use of heating and cooling equipment while maintaining the temperature for maximum comfort. Despite the importance of having an accurate thermal model for the operation of smart thermostats, fast and reliable identification of this model is still an open problem. In this paper, we explore various techniques for establishing a suitable thermal model using time series data generated by smart thermostats. We show that Bayesian neural networks can be used to estimate parameters of a grey-box thermal model if sufficient training data is available, and this model outperforms several black-box models in terms of the temperature prediction accuracy. Leveraging real data from 8,884 homes equipped with smart thermostats, we discuss how the prior knowledge about the model parameters can be utilized to quickly build an accurate thermal model for another home with similar floor area and age in the same climate zone. Moreover, we investigate how to adapt the model originally built for the same home in another season using a small amount of data collected in this season. Our results confirm that maintaining only a small number of pre-trained thermal models will suffice to quickly build accurate thermal models for many other homes, and that 1~day smart thermostat data could significantly improve the accuracy of transferred models in another season. 10 pages

Abstract There has been an increasing interest in addressing and discovering the factors influencing the households’ load profiles instead of their end-use energy demand. The rationale behind this tendency is to provide households with load shifting recommendations and flatten the load profiles by making use of the knowledge obtained from these temporal and contextual determinants. Methodologies connecting households’ activities and presence to load profiles are often under-investigated, and the flexibility in the presence and activity routines of households throughout a long period is ignored. In this study, a data-driven framework is developed to extract households’ daily occupancy patterns throughout a year, determine the regular high- and low-energy consumption periods, and discover influencing activity factors of energy consumption within the obtained periods. The purpose of this study is to provide households with customized load-shifting and energy-saving suggestions based on their specific traits and routines. The results suggest that the distribution of occupancy patterns between seasons and weekdays varies considerably among different households. It is further recognized that days with similar occupancy patterns can have nearly similar peak timings in different apartments. The developed framework is generic and can be generalized to different households with different presence and activity routines.

Abstract Occupancy detection and estimation are two of the main areas of research in smart buildings. This is due to its significant effect in the deployment of energy saving buildings and various other futuristic user-centered applications. Occupancy detection refers to the determination of presence of occupants in a space or smart building, whereas occupancy estimation represents the exact determination of the number of occupants. Recently, machine learning approaches have made momentous advancements in the smart buildings field. Hidden Markov models (HMMs) have played an important role in such amelioration. Consequently, in this paper, we propose and comprehensively study a novel approach for the employment of HMMs in occupancy applications of smart buildings. In particular, the benchmark approach in the literature relies on a state-based model deployment. Hence, an implicit assumption is made that the states would automatically represent the physical phenomena of the data. This is not always the case. Our proposed framework promises a scalable stable deployment of HMMs, particularly in relation to the status quo, with model selection criteria for the determination of the number of states. Our extensive experiments show considerable improvement across the various evaluation metrics. Finally, this work also establishes multiple promising venues of future investigation for practitioners and researchers in the field, of which we discuss some of, especially at the intersection of machine learning and smart buildings.

Abstract Heat-related mortality (HRM) is increasing because of the climate change and urbanization leading to extreme heat events. This paper summarizes the results of the excess mortality attributed to excessive heat events in two largest cities in Canada, Toronto and Montreal, during three heat wave periods. We present an application of a fine-resolution, urban-mesoscale model to assess the impacts of heat and heat mitigation strategy on heat death. The Weather Research and Forecasting model (WRF) is coupled with a multi-layer of the Urban Canopy Model (ML-UCM) to assess the impacts of heat and heat mitigation strategy on heat -related death. The background albedo of 0.2 for urban surfaces are respectively increased to 0.65, 0.60, and 0.45 for roofs, walls, and grounds. The changes of the air mass category, ambient and apparent temperatures interpret the impacts of extreme heat and the potential of increasing surface albedo (ISA) on HRM. Here, the calculations and estimations of HRM is based on the data obtained from Canadian Environmental Health Atlas (CEHA) indicating an average of 120 heat-induced deaths in Toronto and Montreal. ISA affords a reduction in air temperature (1–2 °C), a decrease in dew point temperature (0.2–0.5 °C), and a slight increase in near-surface wind speed (−0.01 to −0.4 m/s). Increase in albedo shifts days into more benign conditions by nearly 60%. The HRM will lessen by 3–7%, pointing that seven to eighteen lives could be saved. Cooling the urban climate will improve discomfort index, lessen the impacts of elevated temperature, enhance human thermal comfort, and decrease HRM to some significant extent.

Abstract Buildings are responsible for a large portion of global energy consumption. Therefore, a detailed investigation towards a more effective energy performance of buildings is needed. Building energy performance is mature in terms of parameters related to the buildings’ physical characteristics, and their attributes are easily collectable. However, the poor ability of emulating reality pertinent to time-dependent parameters, such as occupancy parameters, may result in large discrepancies between estimated and actual energy consumption. Although efforts are being made to minimize energy waste in buildings by applying different control strategies based on occupancy information, new practices should be examined to achieve fully smart buildings by providing more realistic occupancy models to reflect their energy usage. This paper provides a comprehensive review of the methods for collection and application of occupancy-related parameters affecting total building energy consumption. Different occupancy-based control strategies are investigated with emphasis on heating, ventilation, and air conditioning (HVAC) and lighting systems. The advantages and limitations of existing methods are outlined to identify the gaps for future research.

Abstract Rooftop photovoltaic (PV) system, as part of the renewable energy development strategy to guarantee energy security and reduce greenhouse gas emissions in urban areas, has received a lot of attention during the last decade. To provide an up-to-date and systematic research landscape of the rooftop PV field, this study conducted the bibliometric analysis, collaboration network analysis, co-citation analysis, and hotspots detection based on 595 articles collected from the core collection database of Web of Science. The results showed that the number of publications per year in this field has increased steadily since 2015. The USA was the most important contributor in this research field in terms of quantity (number of publications) and impact (number of citations). The co-authorship communities were obtained by collaboration network analysis, and the international collaboration is expected to be further strengthened according to the research focuses of each community. The key knowledge base and the main hot topics of the rooftop PV research field were identified from co-citation analysis and keywords co-occurrence network. Furthermore, based on the literature review, a detailed analysis of the main topics was provided for a better understanding of the current research trends and opportunities. This study can be served as a strategic review of the rooftop PV field to help relevant researchers carry out in-depth research in the future.

Abstract In this paper we undertake a comprehensive study to meet the building heating/cooling and power demand through a sustainable operation. We integrated polymer electrolyte membrane fuel cell (PEMFC) system and triple effect absorption refrigeration system (TEARS) for space cooling/heating and water heating applications in buildings. The analysis is carried out to observe the effects of different operating conditions on the efficiency of the fuel cell, output of the fuel cell and TEARS, and the utilization factor of the system. It is found that the efficiency, the utilization factor, and change in temperature of hot water increases from 36% to 48.8%, 49% to 86%, and 14 K to 23 K, respectively when the temperature of the cell is increased for different cooling loads and membrane thicknesses. In addition, the increase in membrane thickness affected the efficiency, the utilization factor, and change in temperature of hot water in a negative way and they were found to be decreasing from 47.3% to 42%, 85% to 49%, and 23 K to 12 K, respectively for different cooling loads. The water supplied to the house is obtained from a geothermal water source which makes the system more sustainable.

Abstract Techno-economic impact of retrofitting houses in the Canadian housing stock with PV and BIPV/T systems is evaluated using the Canadian Hybrid End-use Energy and Emission Model. Houses with south, south-east and south-west facing roofs are considered eligible for the retrofit since solar irradiation is maximum on south facing surfaces in the northern hemisphere. The PV system is used to produce electricity and supply the electrical demand of the house, with the excess electricity sold to the grid in a net-metering arrangement. The BIPV/T system produces electricity as well as thermal energy to supply the electrical as well as the thermal demands for space and domestic hot water heating. The PV system consists of PV panels installed on the available roof surface while the BIPV/T system adds a heat pump, thermal storage tank, auxiliary heater, domestic hot water heating equipment and hydronic heat delivery system, and replaces the existing heating system in eligible houses. The study predicts the energy savings, GHG emission reductions and tolerable capital costs for regions across Canada. Results indicate that the PV system retrofit yields 3% energy savings and 5% GHG emission reduction, while the BIPV/T system yields 18% energy savings and 17% GHG emission reduction in the Canadian housing stock. While the annual electricity use slightly increases, the fossil fuel use of the eligible houses substantially decreases due to BIPV/T system retrofit.