Search Results (2)

This study focused on optimizing a model house for different locations and types of thermal systems to understand better how heating system type affects thermal envelope design, operational greenhouse gas emissions, and life-cycle cost. The study investigated six different thermal system configurations in separate optimizations for five locations. Optimization implies reducing energy consumption, minimizing greenhouse gas emissions (GHG), lowering operational costs, ensuring regulatory compliance, enhancing resilience, and improving occupant comfort and health. The Pareto front, multi-objective optimization, is used to identify a set of optimal solutions, considering multiple goals that may conflict with each other. In determining the least-cost building design envelope, the design balances costs with other goals, such as energy efficiency and environmental impact. The optimizations determine the life-cycle cost versus operational GHG emissions for a single-detached house in Canadian locations with varying climates, emissions factors, and energy costs. Besides natural gas, the study evaluated four electricity-heated options: (a) an air-source heat pump, (b) a ductless mini-split heat pump, (c) a ground-source heat pump, and (d) an electric baseboard. A net-zero-carbon design with grid-tied photovoltaics was also optimized. Results indicate that the heating system type influences the optimal enclosure design. In each location, at least one all-electric kind of design has a lower life-cycle cost than the optimized gas-heated model, and such designs can mitigate the majority of operational GHG emissions from new housing in locations with a low carbon electricity supply. Full article

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses. Full article