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Radiative cooling uses high-emissivity materials to passively cool the surface of outdoor objects, such as building roofs on hot days. The issue of overcooling with this technology on cold days can be addressed by structures with thermal emissivity that is adaptive to temperature. Despite recent advances in temperature-adaptive structures, great challenges remain in their fabrication feasibility and unoptimized solar heating that may override the radiative cooling benefit. In this work, we develop a printable, emissivity-adaptive and albedo-optimized covering (PEAC) based on recyclable materials with roll-to-roll fabrication. A PEAC automatically switches its sky-window emissivity from 0.25 to 0.85 when the surface temperature exceeds a pre-set transition temperature, delivering an albedo optimized for maximal year-round energy saving or thermal comfort in a given climate.

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

•A steep increase in publications linking urban models with energy systems and urban energy building models.•Ambiguous definition of urban morphology and its parameters/indicators tailored for energy system optimization.•Poor conveyance of climate model results to energy models or generating realistic future weather datasets for energy and resilience analyses.•Suggesting an urban morphology spatial scale and a climate model classification tailored to urban energy systems.•Framework proposed to link urban-climate-energy models for designing sustainable urban energy infrastructure under the impacts of climate change.

Over the past four decades, a substantial body of literature has explored the impacts of occupant behaviour (OB) on building technologies, operation, and energy consumption. A large number of data-driven behavioural models have been developed based on field data. These models lack standardisation and consistency, leading to difficulties in applications and comparison. To address this problem, an ontology was developed using the drivers-needs-actions-systems (DNAS) framework. Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language (XML) schema, titled ‘occupant behaviour XML’ (obXML) which is a practical implementation of OB models that can be integrated into building performance simulation (BPS) programs. This paper presents a newly developed library of OB models represented in the standardised obXML schema format. This library provides ready-to-use examples for BPS users to employ more accurate occupant representation in their energy models. The library, which contains an initial effort of 52 OB models, was made publicly available for the BPS community. As part of the library development process, limitations of the obXML schema were identified and addressed, and future improvements were proposed. Authors hope that by compiling this library building, energy modellers from all over the world can enhance their BPS models by integrating more accurate and robust OB patterns.

Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social and local comfort parameters. In this study, 33 projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field, and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.

•Developed an interdisciplinary framework integrating three theories (SCT, DNAS Framework, TPB).•The framework investigates the building-user interaction in offices.•Focus on energy-related interactions influencing occupant comfort and energy cost.•An online cross-country survey including 37 questions is designed in Qualtrics.•The survey targets workspaces and cultural contexts in US, China, Europe, and Australia.

•EFOnt, a semantic ontology for building energy flexibility applications, is introduced.•EFOnt is designed to work with other building energy data tools as part of an ecosystem.•EFOnt standardizes energy flexibility definition, characterization, and quantification.•One measurement-based and one simulation-based use case of EFOnt are given.•The future development and potential use cases of EFOnt are discussed.

•Introduces a novel demand flexibility (DF) benchmarking metrics framework.•Allows benchmarking across geographic areas with different climates and grid needs.•Enables empirical approaches to achieve understanding of key influential factors of DF.•Framework tested with two benchmarking case studies of 132 office and retail buildings.•Identified load shed intensity range and variability; influence of event duration and timing.

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

•Review of applications of transfer learning (TL) for smart buildings.•Identification of main application areas of TL in smart buildings.•Insights on the most-effective TL techniques for each application area.•Discussion on current research gaps and future opportunities.

•The work is the first application of CityBES tool to an Italian historic district.•Urban building simulations were performed despite the difficulty of obtaining data.•The buildings’ dataset is a low-income housing district in Venice constructed immediately after WWI.•Four actions are applied achieving high energy savings and emission reduction.•The methodology is replicable in other historic district-level retrofit.

•Open-source virtual testbed is crucial to benchmark and evaluate control algorithms.•A realistic virtual testbed AlphaBuilding ResCommunity is created using OpenAI Gym Interface.•The parameters of this testbed are learned from a connected smart thermostat dataset.•Kalman Filter and Model Predict Control are implemented and tested for single households.•PSBC and ADMM are implemented to coordinate electric loads of multiple households.

•The concept of the urban cell is introduced.•Novel concept help to design interconnected energy infrastructure.•Both sectoral and spatial coupling is considered.•A novel computational model based on modular approach is introduced.•It was shown urban cell concept reduces cost by 37%.•It was shown urban cell concept increases renewable energy capacity by 25%.

•We studied how city electricity use is influenced by weather and COVID-19 pandemic.•Seven data-driven models were applied and evaluated for data of three cities.•Gradient boosting tree model delivers the most accurate prediction with CVRMSE of 4%−6%.•1 °C increase of ambient temperature drives up the three cities electricity usage by around 5%.•COVID-19 curtailment reduced city-scale electricity usage by 2%−12%.

•An energy modeling analysis for cost-effective near-Zero Net Energy home designs•Time of use energy valuation considered in the definition and modeling of NZEB•NZEB homes integrate energy efficiency technologies, solar PV, and electric batteries•All-electric homes are comparable in costs to mixed-fuel homes in most of California

•Studies on energy flexibility of residential buildings have been reviewed.•Energy flexibility technologies by types, purposes, and scopes are summarized•Modeling techniques, tools, and characterization of flexible resources are presented.•Quantification methods and metrics of energy flexibility are analyzed.•Research trends, open questions, and future research opportunities are identified.

This study investigates human-building interaction in office spaces across multiple countries including Brazil, Italy, Poland, Switzerland, the United States, and Taiwan. We analyze social-psychological, contextual, and demographic factors to explain cross-country differences in adaptive thermal actions (i.e. cooling and heating behaviors) and conformity to the norms of sharing indoor environmental control features, an indicator of energy consumption. Specifically, personal adjustments such as putting on extra clothes are generally preferred over technological solutions such as adjusting thermostats in reaction to thermal discomfort. Social-psychological factors including attitudes, perceived behavioral control, injunctive norms, and perceived impact of indoor environmental quality on work productivity influence occupants’ intention to conform to the norms of sharing environmental control features. Lastly, accessibility to environmental control features, office type, gender, and age are also important factors. These findings demonstrate the roles of social-psychological and certain contextual factors in occupants’ interactions with building design as well as their behavior of sharing environmental control features, both of which significantly influence building energy consumption, and thus, broader decarbonization.