Facilitating energy savings with programmable thermostats: evaluation and guidelines for the thermostat user interface.

Thermostats control heating and cooling in homes - representing a major part of domestic energy use - yet, poor ergonomics of these devices has thwarted efforts to reduce energy consumption. Theoretically, programmable thermostats can reduce energy by 5-15%, but in practice little to no savings compared to manual thermostats are found. Several studies have found that programmable thermostats are not installed properly, are generally misunderstood and have poor usability. After conducting a usability study of programmable thermostats, we reviewed several guidelines from ergonomics, general device usability, computer-human interfaces and building control sources. We analysed the characteristics of thermostats that enabled or hindered successfully completing tasks and in a timely manner. Subjects had higher success rates with thermostat displays with positive examples of guidelines, such as visibility of possible actions, consistency and standards, and feedback. We suggested other guidelines that seemed missing, such as navigation cues, clear hierarchy and simple decision paths. PRACTITIONER SUMMARY: Our evaluation of a usability test of five residential programmable thermostats led to the development of a comprehensive set of specific guidelines for thermostat design including visibility of possible actions, consistency, standards, simple decision paths and clear hierarchy. Improving the usability of thermostats may facilitate energy savings.

Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate.

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

Two (or more) for one: Identifying classes of household energy- and water-saving measures to understand the potential for positive spillover.

A key component of behavior-based energy conservation programs is the identification of target behaviors. A common approach is to target behaviors with the greatest energy-saving potential. The concept of behavioral spillover introduces further considerations, namely that adoption of one energy-saving behavior may increase (or decrease) the likelihood of other energy-saving behaviors. This research aimed to identify and describe household energy- and water-saving measure classes within which positive spillover is likely to occur (e.g., adoption of energy-efficient appliances may correlate with adoption of water-efficient appliances), and explore demographic and psychographic predictors of each. Nearly 1,000 households in a California city were surveyed and asked to report whether they had adopted 75 different energy- and/or water-saving measures. Principal Component Analysis and Network Analysis based on correlations between adoption of these diverse measures revealed and characterized eight water-energy-saving measure classes: Water Conservation, Energy Conservation, Maintenance and Management, Efficient Appliance, Advanced Efficiency, Efficient Irrigation, Green Gardening, and Green Landscaping. Understanding these measure classes can help guide behavior-based energy program developers in selecting target behaviors and designing interventions.

The field of human building interaction for convergent research and innovation for intelligent built environments.

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications.

Animal experimentation is key in the evaluation of cardiac efficacy and safety of novel therapeutic compounds. However, interspecies differences in the mechanisms regulating excitation-contraction coupling can limit the translation of experimental findings from animal models to human physiology and undermine the assessment of drugs' efficacy and safety. Here, we built a suite of translators for quantitatively mapping electrophysiological responses in ventricular myocytes across species. We trained these statistical operators using a broad dataset obtained by simulating populations of our biophysically detailed computational models of action potential and Ca2+ transient in mouse, rabbit, and human. We then tested our translators against experimental data describing the response to stimuli, such as ion channel block, change in beating rate, and ß-adrenergic challenge. We demonstrate that this approach is well suited to predicting the effects of perturbations across different species or experimental conditions and suggest its integration into mechanistic studies and drug development pipelines.

Home energy management (HEM) database: A list with coded attributes of 308 devices commercially available in the US.

Policymakers worldwide are currently discussing whether to include home energy management (HEM) products in their portfolio of technologies to reduce carbon emissions and improve grid reliability. However, very little data is available about these products. Here we present the results of an extensive review including 308 HEM products available on the US market in 2015-2016. We gathered these data from publicly available sources such as vendor websites, online marketplaces and other vendor documents. A coding guide was developed iteratively during the data collection and utilized to classify the devices. Each product was coded based on 96 distinct attributes, grouped into 11 categories: Identifying information, Product components, Hardware, Communication, Software, Information - feedback, Information - feedforward, Control, Utility interaction, Additional benefits and Usability. The codes describe product features and functionalities, user interaction and interoperability with other devices. A mix of binary attributes and more descriptive codes allow to sort and group data without losing important qualitative information. The information is stored in a large spreadsheet included with this article, along with an explanatory coding guide. This dataset is analyzed and described in a research article entitled "Categories and functionality of smart home technology for energy management" (Ford et al., 2017) [1].