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The following Key Messages comprise the salient findings of this study:1. Ambient energy (from sun, air, ground, and sky) can heat and cool buildings; provide hot water, ventilation, and daylighting; dry clothes; and cook food. These services account for about three-quarters of building energy consumption and a third of total US demand. Biophilic design (direct and indirect connections with nature) is an intrinsic adjunct to ambient energy systems, and improves wellness and human performance.2. The current strategy of electrification and energy efficiency for buildings will not meet our climate goals, because the transition to an all-renewable electric grid is too slow. Widespread adoption of ambient energy is needed. Solar-heated buildings also flatten the seasonal demand for electricity compared to all-electric buildings, reducing required production capacity and long-term energy storage. In addition, ambient-conditioned buildings improve resilience by remaining livable during power outages.3. National policies, incentives, and marketing should be enacted to promote ambient energy use. Federal administrative priorities should reflect the importance of ambient energy for buildings. Use of ambient energy should be encouraged through existing and new building codes and standards.4. Ambient energy system design tools are needed for architects, engineers, builders, building scientists, realtors, appraisers, and consumers. PVWatts is used over 100 million times per year for photovoltaic system design. A similar, simple, and accessible tool for ambient design is crucial.5. Training on ambient energy is needed throughout secondary, post-secondary, and continuing education for workforce development. Currently, only about 10% of colleges teach courses on passive heating and cooling systems.6. Ambient-conditioned buildings should be demonstrated in all US climate zones. Performance should be monitored and reported, with quantitative case studies made widely available.7. While current technology is sufficient to build high-performance ambient buildings now, research is needed to develop new technologies to harness ambient energy more effectively and more economically. Such advancements will facilitate adoption of ambient energy technologies in a wider range of buildings, including retrofits. Examples include windows with much lower thermal losses, use of the building shell for thermal storage, alternative light-weight thermal storage systems, sky-radiation cooling systems, automated controls for solar gains and passive cooling, and ground coupling.

Abstract We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

Densely built areas with poor thermal insulation suffer from high thermal environmental risks and generally consume high energy in summer. Determining the relationship between density and energy consumption is necessary, particularly when implementing urban heat island (UHI) countermeasures. This study evaluated the effects of density and UHI countermeasures on the energy consumption and indoor thermal comfort of a detached house in a typical densely built wooden house area in Yokohama City, Japan. Three densities and six countermeasures were considered. Annual hourly simulations based on the SCIENCE-Vent thermal environment simulation model yielded the following results: in densely built wooden house areas, the energy consumption and thermal discomfort increased with density. The green roof yielded the largest energy savings in the cooling and heating seasons, demonstrating the highest annual energy savings with 5.7%. Density had little impact on rooftop countermeasures, but the effect of the high-reflectance walls increased with density, and the reduction in annual energy consumption (air conditioning and lighting) is 2.6%, 3.0%, 3.6% in 37%, 47%, and 59% density cases, respectively. The impact of thermal countermeasures on indoor thermal comfort varied according to the thermal control mechanism.

Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.