Demand Side Management (DSM) is a cost-effective approach to managing electricity networks, aimed at reducing capacity requirements and costs, increasing the penetration of renewable generation, and reducing power system emissions. This review article explores the distinctive characteristics of electricity demand in the industrial, commercial, and residential sectors, and their relationship to successful implementation of DSM. The constraints and considerations for DSM are characterized as technical, economic, and behavioral factors, such as process requirements, business operation constraints, and consumer decisions, respectively. By considering all three types of factors and their impacts in each sector, this review contributes novel insights that can inform the future implementation of DSM. DSM in the industrial and commercial sectors is found to be primarily constrained by technical considerations, while DSM in the commercial sector is also subject to economic constraints. Conversely, residential demand is found to be primarily constrained by human behavior and outcomes, highly variable, and the largest contributor to peak demand. This review identifies sector-specific opportunities to enhance DSM uptake. Industrial DSM uptake will benefit from technological and process improvements; commercial DSM uptake can benefit from enhanced economic incentivization; and residential DSM uptake can benefit from improved understanding of the interactions between human behavior, human outcomes, and energy use. Finally, this review investigates behavioral models and concludes that agent-based models are best suited for integrating these interactions into energy models, thereby driving the uptake of DSM, particularly in the important residential sector.

Imaging photometers and ray-tracing software packages have made it possible to capture and model high-resolution and accurate luminance maps. However, luminance map measurement is rarely seen in professional practice, despite its ability to evaluate visual parameters accurately and directly, such as contrast, visual, size, and target brightness. Two barriers to the uptake of luminance measurement and associated design measures include (1) lack of knowledge of the range of measures available, and (2) difficulty in assessing whether a luminance-based lighting design method is a sufficient and justifiable replacement for the current illuminance-based practice. This paper reviews current practice and presents alternative luminance design measures and human needs for lighting to construct a framework for designing and comparing lighting design methods. It concludes by presenting a new luminance-based lighting method in the context of this framework to show that it is more accurate and comprehensive than current practice and can be enabled by emerging low-cost and increasingly accessible luminance measurement technologies. The overall outcomes provide the metrics and framework to bring more complete and effective luminance-based lighting design into practice.

Level of detail (LoD) is an important factor in urban building energy modelling (UBEM), affecting functionality and accuracy. This work assesses the impacts of the LoD of the roof, window, and zoning on a comprehensive range of outcomes (annual heating load, peak heating demand, overheating, and time-series heating error) in a representative New Zealand house. Lower-LoD roof scenarios produce mean absolute error results ranging from 1.5% for peak heating power to 99% for overheating. Windows and shading both affect solar gains, so lower-LoD windows and/or shading elements can considerably reduce model accuracy. The LoD of internal zoning has the greatest effect on time-series accuracy, producing mean absolute heating error of up to 66 W. These results indicate that low-LoD “shoebox” models, common in UBEM, can produce significant errors which aggregate at scale. Accurate internal zoning models and accurate window size and placement have the greatest potential for error reduction, but their implementation is limited at scale due to data availability and automation barriers. Conversely, modest error reductions can be obtained via simple model improvements, such as the inclusion of eaves and window border shading. Overall, modellers should select LoD elements according to specific accuracy requirements.