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[Display omitted] • The paper reviews 43 articles defining methodologies for climate classification. • The paper provides overview of climate classification and validation methods. • Need of standard methodology or nomenclature for climate classification. • The optimal approach for conducting climate zoning remains a topic of debate. Climate classification simplifies the representation of expected climate conditions by eliminating the need to describe each climatic parameter. Climate classification is frequently used as a basis for recommendations through codes and standards for buildings because a given climate has nearly similar design conditions. Furthermore, building energy efficiency programmes are designed based on the climatic classification to determine appropriate design strategies to eliminate the under-design or over-design of buildings for similar climatic regions. This necessitates the use of realistic and reliable climate classifications. Over the past two decades, numerous research studies have proposed climate classification methods. However, there is a lack of universal acceptance of climate classification methodology to balance complexity and accuracy. This research provides an overview of various climate classification methods and indicators used to assess the significance of climatic conditions. Moreover, this study highlights pressing issues, proposes a feasible climate classification methodology, and offers substantial possibilities for future study. This study organizes and evaluates the various classification and validation methods used by researchers, variables, and the granularity of data used for the classification. The usual criteria for defining a climate are temperature, relative humidity, cooling-degree days, heating-degree days, and solar radiation. About eighty-five percent of the classified climates are using at most three variables. The number and characteristics of climate zones vary with changes in the resolution of climate data, classification methods, and the inclusion of different sets of parameters. Clustering methods are becoming more important for climate classification as these methods can handle multiple variables more accurately. To tackle the complex relationship between climate and building thermal design, it is recommended to use climate variables with cluster analysis and validation based on thermal loads. This paper will give researchers an aerial perspective of the scope and direction of ongoing research in this field. [ABSTRACT FROM AUTHOR]

Optimizing efficiency in the operation of the HVAC system of existing buildings requires the construction of a thermal dynamic model of the building, which may be challenging because architectural metadata may be missing or obsolete. Based on a suitable set of measured data, this paper presents a novel practical methodology to create and automatically derive thermal models of existing buildings in high-fidelity simulators for energy management. To this end, the philosophy of grey-box strategies is followed to simplify the modeling and avoid the requirement of architectural metadata, facilitating and expediting the process. First, a building model with a highly reduced number of parameters is constructed by exploiting the existing similarities in the materials of the buildings and simplifying their elements to a simple one-layer parameterization. Then, the parameters of the derived model are iteratively updated while minimizing the error between the real temperature evolution and that generated by the model being identified. For this purpose, data of the room air temperature, estimated occupancy, weather conditions, and variables of the HVAC system are assumed to be available in suitable zones of the building to apply the creation and identification processes of the model, allowing that a whole digital twin of the building is constructed. The methodology is presented by its application to a real case study: the Nimbus Research Centre building at Munster Technological University, located in Cork (Ireland). The high-fidelity simulator software TRNSYS is used for the modeling task, together with the GenOpt optimization program. The results demonstrate that the proposed methodology yields a highly accurate model of the building, capable of representing reality with RMSE values consistently below 0.6 C ∘ during open-loop validation periods of up to four days. The findings suggest that this methodology may outperform other modeling techniques reported in the literature. Importantly, the proposed technique is less complex and time-consuming to implement than many of the alternatives. • Thermal modeling of existing buildings avoiding the use of architectural metadata. • Novel integration of grey-box modeling methods with high-fidelity simulators. • The proposed method copes with the drawbacks of the models found in the literature. • Methodology validated with experimental data from the Nimbus Centre (Ireland). • Results show high accuracy of greatly simplified models—RMSE<0.6 °C in 4-day horizons. [ABSTRACT FROM AUTHOR]

[Display omitted] • 30,000 full-text building-related articles have been extracted for text mining. • Data-science, energy efficiency, and lifecycle phases relations have been drawn. • Word embeddings model has been used to extract the relationship between keywords. • Gaps, opportunities, and potential future directions have been discussed. The ever-changing data science landscape is fueling innovation in the built environment context by providing new and more effective means of converting large raw data sets into value for professionals in the design, construction and operations of buildings. The literature developed due to this convergence has rapidly increased in recent years, making it difficult for traditional review approaches to cover all related papers. Therefore, this paper applies a natural language processing (NLP) method to provide an exhaustive and quantitative review.Approximately 30,000 scientific publications were retrieved from the Elsevier API to extract the relationship between data sources, data science techniques, and building energy efficiency applications across the life cycle of buildings. The text-mining and NLP analysis reveals that data sciences techniques are applied more for operation phase applications such as fault detection and diagnosis (FDD), while being under-explored in design and commissioning phases. In addition, it is pointed out that more data science techniques that are to be investigated for various applications. For example, generative adversarial networks (GANs) has potential in facilitating parametric design; transfer learning is a promising path to promoting the application of optimal building operation; [ABSTRACT FROM AUTHOR]

In this paper a techno-economic evaluation method for deep renovation of buildings is introduced. We study the validation of an integrated design methodology based upon the creation of additional volume on existing buildings (Add-Ons) intended as roof-top or aside extensions, enclosures, parasites or façade transformations. Energy price uncertainty is modelled through a mean-reverting stochastic process, which well fits the data for natural gas prices, and the proposed evaluation method is based on real-option theory, which is an advancement of standard cost-benefit analysis, to be employed whenever uncertainty has a significant impact on the investment option. The new method to evaluate the economical feasibility of the Add-ons strategy for Deep Energy Renovation is tested through design experiments on a real case study of social housing in Reggio Emilia (Italy). [ABSTRACT FROM AUTHOR]

Building operations account for the largest proportion of energy use throughout the building life cycle. The energy saving potential is considerable taking into account the existence of a wide variety of building operation deficiencies. The advancement in information technologies has made modern buildings to be not only energy-intensive, but also information-intensive. Massive amounts of building operational data, which are in essence the reflection of actual building operating conditions, are available for knowledge discovery. It is very promising to extract potentially useful insights from big building operational data, based on which actionable measures for energy efficiency enhancement are devised. Data mining is an advanced technology for analyzing big data. It consists of two main types of data analytics, i.e., supervised and unsupervised analytics. Despite of the power of supervised analytics in predictive modeling, unsupervised analytics are more practical and promising in discovering novel knowledge given limited prior knowledge. This paper provides a comprehensive review on the current utilization of unsupervised data analytics in mining massive building operational data. The commonly used unsupervised analytics are summarized according to their knowledge representations and applications. The challenges and opportunities are elaborated as guidance for future research in this multi-disciplinary field. [ABSTRACT FROM AUTHOR]

In this paper, a representative office building zone with an electrochromic (EC) glazed façade was simulated in TRNSYS and Radiance/Daysim for a large number of different combinations of design parameters (i.e. location, façade orientation, window control, window-to-wall ratio, internal gains, thermal mass and envelope air tightness). Results of energy consumption, peak energy demand, useful daylight index (UDI) and predicted percentage of persons dissatisfied (PPD) for a total of 7680 scenarios were obtained and used in a sensitivity analysis considering the Main effect of the building parameters. The relative influence of the parameters is presented and the different designs improving the outputs are determined. Results have shown that the greatest total energy savings considering EC windows are for warmer climates with higher solar radiation exposures. The presence of an EC window mostly influences the cooling peak load and acts as an alternative solution to thermal mass from the perspective of peak reductions. While the choice of the specific window control strategy is having a limited impact on the energy savings and peak load reductions, the analysis revealed that this parameter has a larger impact on the visual comfort (UDI). The use of smart window does not appear to greatly influence the thermal comfort within the zone (small impact on the PPD). [ABSTRACT FROM AUTHOR]

Building sector accounts for significant portion of total final energy use in most countries. One way to reduce significantly building energy consumption is to adopt energy efficiency technologies and strategies. Due to environmental concerns and high cost of energy in current years there has been a renewed interest in building energy efficiency. Advanced control strategies provide a more efficient way of minimizing energy demand of buildings and maintaining indoor environmental quality in accordance with global principle of sustainability, which has also proven reliable for diverse applications such as Heating, Ventilation and Air Conditioning (HVAC) control and thermal comfort control etc. The objective of this paper is to review the control strategies in buildings, particularly focusing on low energy buildings (LEB), in recent 10 years. Present work consists of why to use control strategies in buildings, categories of control strategies, research literature for building performance affected by diverse control strategies from the perspective of theoretical modelling, physical experimental study and numerical simulation investigation. Following that, more than 20 parameters affecting control performance have been analyzed and evaluated. The literature has illustrated that the best overall performance of control strategy is proportional integral derivative control-model predictive control (PID-MPC); the effects of those parameters on control performance are completely different, depending on which exact initial conditions and the interaction between different factors. [ABSTRACT FROM AUTHOR]

Climatic zoning for building energy efficiency applications is a key element in many programs and policies to improve thermal performance of buildings. In spite of its importance, there is no consensus about the appropriate methodology for climatic zoning. Previous studies indicate a large variety of methods and parameters are currently used for climatic zoning: degree-days, cluster analysis and administrative divisions are some of the most widely used. This study reports and reviews results obtained with these three methodologies for Nicaragua, a small country in Latin America. Results indicate a high level of agreement between the different methodologies, but they also disagree on the appropriate classification of a significant proportion of the country (37% of Nicaragua’s territory). The three methodologies have strengths and weaknesses, and at present it is impossible to conclude which one is the most appropriate to support building energy efficiency programs and policies. Results of this paper highlight the need for procedures and performance indicators to assess the validity of climatic zoning (which shall be addressed by future studies). [ABSTRACT FROM AUTHOR]

Retrofitting existing buildings has emerged as a primary strategy for reducing energy use and carbon emissions, both nationally and in cities. Despite the increasing awareness of retrofitting opportunities and a growing portfolio of successful case studies, little is known about the decision-making processes of building owners and asset managers with respect to energy efficiency investments. Specifically, the research presented here examines the effects of ownership type, tenant demand, and real estate market location on building energy retrofit decisions in the commercial office sector. This paper uses an original, detailed survey of asset managers of 763 office buildings in nineteen cities sampled from the CBRE, Inc. portfolio. Controlling for various building characteristics, the results demonstrate that ownership type and local market do, in fact, influence the retrofit decision. Overall, this analysis provides new evidence for the importance of understanding ownership type and the varying motivations of differing types of owners in building energy efficiency investment decisions. The findings of both the survey analysis and the predictive model demonstrate additional support for the targeting of energy efficiency incentives and outreach based on ownership entity, local market conditions, and specific physical building characteristics. [ABSTRACT FROM AUTHOR]

[Display omitted] • Various elements influence micro-climate around building and impact its energy use. • Micro-climatic impact on buildings in sub-tropical/tropical zones is rarely studied. • Passivhaus building performance in sub-tropical/tropical zones is under examined. • Passivhaus buildings are sensitive to surrounding micro-climates in temperate zones. • Digitised Eco-feedback is effective in increasing energy consumption awareness. The earth's climate differs enormously across its breadth with numerous small-scale variations to surrounding atmospheric conditions (micro-climates) existing side by side. These differences impact indoor environments of residential buildings and, depending on occupants' preferences, building energy use, and can mean that home designs are unfit for the local context. Although considered leaders in producing energy efficient designs which are equally comfortable for occupants and the environment, even Passivhaus (PH) homes have been demonstrated to be sensitive to localised outdoor conditions. Meanwhile, occupants have little understanding of their own domestic energy use, how the situation of their property might impact their comfort, and of more sustainable alternatives open to them. To boost understanding of household energy use and to encourage energy conservation, instantaneous computerised feedback on household's energy consumption (Eco-feedback) has been widely used. Along with improving household energy use understanding, Eco-feedback technologies may assist in comprehending local micro-climatic conditions around a building, and in learning how to manipulate these can help to establish and sustain preferred indoor conditions with minimal energy use. This can be achieved through producing personalised and contextualised feedback and responses to update and renovate users' homes by suggesting short-term actions and long-term design recommendations. This systematic literature review paper explores the breadth of Eco-feedback techniques, their relative benefits and shows how these could be deployed in the context of understanding building performance in the home. [ABSTRACT FROM AUTHOR]

• A systematic literature review on the SA for building application was provided. • Key information about the SA case studies was extracted, summarized, and analyzed. • Pros and cons of the mainstream methods for SA and sampling were summarized. • Practical advice on the selection of the sampling and SA methods were given. A building system is highly nonlinear and commonly includes a variety of parameters ranging from the architectural design to the building mechanical and energy system. Serving as a powerful tool to understand the complicated building system, sensitivity analysis (SA) has been receiving increasing attention in the recent decade. This review paper focused on the application of SA in the building performance analysis (BPA). First, the existing review papers on the application of SA for BPA were briefly reviewed and summarized. Then, a large amount of recent SA case studies in BPA were reviewed. The critical information regarding the implementation of a SA, such as the sampling method, the SA method, etc., were extracted and summarized. Next, an extensive analysis was performed to evaluate the role of the SA in the BPA. This includes: 1) The typical selection of the input and output parameters for the SA were visualized; 2) The uncertainty level and sampling strategies for the input parameters were investigated; 3) The principles, benefits, and drawbacks of the SA methods were analyzed and summarized; and 4) The various tools used in the SA, including the simulation software, sampling tools, and SA tools were introduced to facilitate the practitioners. Lastly, several conclusions were summarized on the role of the SA in the BPA. Suggestions were also provided to offer practical solutions to the current challenges. [ABSTRACT FROM AUTHOR]

The concept of adaptive thermal comfort was formulated many decades ago and has been validated in numerous field studies. As a result, wider acceptable indoor temperature ranges based on adaptive models have been included in international and national standards and the adaptive approach to thermal comfort is regarded as a significant contributor in achieving low energy building design and operation. Despite the ever-increasing scientific literature on adaptive comfort around the world, the overall understanding of how to translate the adaptive principles into design practice and concepts for operating buildings is still limited, which suggests a gap between the scientific outcomes and the real-world applications. This discussion paper identifies the challenges and gaps in using the principles of adaptive thermal comfort by design practitioners and discusses them in light of relevant research findings. More than 100 literature sources were reviewed in support of the discussion. The paper then proposes a framework that aims to facilitate the adoption of adaptive comfort principles in design and operation of buildings and describes the outline of an imminent guideline for low energy building design based on the concept of adaptive thermal comfort. [ABSTRACT FROM AUTHOR]

• A deep learning-based framework is proposed to estimate building energy efficiency. • Street view images help improve the overall energy efficiency prediction. • Important building attributes have been identified for improving energy efficiency. With buildings consuming nearly 40% of energy in developed countries, it is important to accurately estimate and understand the building energy efficiency in a city. A better understanding of building energy efficiency is beneficial for reducing overall household energy use and providing guidance for future housing improvement and retrofit. In this research, we propose a deep learning-based multi-source data fusion framework to estimate building energy efficiency. We consider the traditional factors associated with the building energy efficiency from the Energy Performance Certificate (EPC) for 160,000 properties (30,000 buildings) in Glasgow, UK (e.g., property structural attributes and morphological attributes), as well as the Google Street View (GSV) building façade images as a complement. We compare the performance improvements between our data-fusion framework with traditional morphological attributes and image-only models. The results show that including the building façade images from GSV, the overall model accuracy increases from 79.7% to 86.8%. A further investigation and explanation of the deep learning model are conducted to understand the relationships between building features and building energy efficiency by using SHapley Additive exPlanations (SHAP). Our research demonstrates the potential of using multi-source data in building energy efficiency prediction with high accuracy and short inference time. Our paper also helps understand building energy efficiency at the city level to help achieve the net-zero target by 2050. [ABSTRACT FROM AUTHOR]

In HVAC control problems, model-based optimal control methods have been extensively studied and verified by many researchers, but they highly depend on the accuracy of the model, a large amount of historical data, and the deployment of different sensors. With respect to the above problems, this paper proposed a Multi-Agent deep reinforcement learning method for the building Cooling Water System Control (MA-CWSC) to optimize the load distribution, cooling tower fan frequency, and cooling water pump frequency of different types of chillers, which provide a model-free and online learning mechanism. In the learning process, unlike the traditional reinforcement learning methods, the proposed control method uses five agents to control different controllable parts by parallel learning, which can greatly reduce the action space and speed up the convergence rate. In order to verify the effectiveness of the propose-d control method, based on the actual building cooling water system parameters and related historical data, we construct an experimental building cooling water system model, and moreover, we test the MA-CWSC method, model-based control method (optimal control method), baseline method, and single-agent deep reinforcement learning (deep Q-network). The experimental results show that the energy saving performance of the proposed MA-CWSC method is significantly better than the rule-based control method (11.1% improvement), and is very close to that of the model-based control method (only 0.5% difference). In addition, the MA-CWSC method has a faster learning rate compared to the deep Q-network (DQN) control method. [ABSTRACT FROM AUTHOR]

Currently, there is no clear and unified understanding about the status quo of China's energy consumption in the building sector. In addition, a considerable underestimation of energy associated with buildings has impeded the effective implementation of measures to improve building energy efficiency of China. Thus, in this paper, we seek to identify the building sector's energy consumption of China by establishing an estimation model of building energy consumption from a life cycle perspective. On the basis of macro-level statistical data and relevant literature, we analyze the activities in each phase and calculate associated energy consumptions throughout buildings’ whole life cycle in China from 2001 to 2013. The results show that China's energy consumption associated with buildings has reached 1.66 billion tons coal equivalent in 2013, with a stable growth rate of 7% annually since 2001. Buildings’ life-cycle energy has approximately accounted for 43% of China's total energy consumption for recent three years (2011–2013). What's more, energy consumption in buildings’ operation phase has been salient, accounting for over 20% of China's total energy consumption. More focus should be drawn on energy efficiency in building material production phase and energy consumed in China's rural residential buildings as both have been significantly neglected. [ABSTRACT FROM AUTHOR]

In both the US and French building sectors, significant efforts have been undertaken to reduce the environmental impacts of new constructions notably their fossil energy consumption and related carbon emissions. This paper summarizes the results of a comparative evaluation of optimal design specifications for office buildings for selected locations in both US and France using life cycle cost analysis approach. First, an overview of both the US and French approaches for developing building energy efficiency standards and design of high performance buildings is outlined. Then, a general approach for design optimization of energy efficient office buildings is presented. Finally, selected results of the optimal analysis are discussed. In particular, it is found that optimizing life cycle costs (LCC) resulted on average in 30% in primary energy savings for office buildings located in the US and 40% savings in France. However, it is found that LCC-based optimal building designs do not meet the French energy efficiency RT 2012 standard. When accounting for typical operating conditions, photovoltaic (PV) panels are needed in order to comply with the RT 2012 required thresholds for the total primary energy consumption of the office buildings located in the French cities considered in the analysis. [ABSTRACT FROM AUTHOR]

Abstracts China's building energy efficiency design standards have been always treated as the base of national energy conservation planning and industrial investment. However, they cannot fully achieve the expected benefits due to the energy rebound effect. This paper verifies the energy rebound effect in China's urban and rural residential buildings based on the LA-AIDS theory, and further estimates the building energy conservation by counterfactual analysis, catching the specific influences of rebound effect on building energy conservation and the corresponding residential building energy conservation potentials of China. The empirical results reveal that: (i) The rebound effect in the rural residential buildings is much larger than that in the urban residential buildings, where presenting the “back-fire” effect. (ii) The rebound effect in the rural residential buildings is weakening while it is enhancing in the urban residential buildings. (iii) Nationally, if energy policies including price polices, technological improvements as well as some other measures were implemented to avoid the rebound effect, we could have conserved about 20% electricity consumption in China's residential buildings each year. The magnitude increased by time to the highest level (107.66 GWh) in 2011, equivalent to the CO 2 emission reduction of 0.1 billion tons. [ABSTRACT FROM AUTHOR]

Chiller sequencing control is an essential function for a multiple-chiller plant that switches on and off chillers in terms of building instantaneous cooing load. It significantly affects both indoor temperature control (hence indoor thermal comfort) and building energy consumption. Various chiller sequencing controls have been developed and implemented in practice, and all of them switch on or off chillers according to a direct or an indirect indicator of the building instantaneous cooling load. Potential uncertainties in the direct or indirect indicator may cause the sequencing control misbehave and deteriorate the control and energy performance of the chiller plant. Until now, there is no any systematic study to investigate those uncertainties. This paper, therefore, proposes such a study. Four typical chiller sequencing controls are considered, including total cooling load-based sequencing control, return water temperature-based sequencing control, bypass flow-based sequencing control, and direct power-based sequencing control. Their uncertainty sources are identified and grouped into different categorizes. In order to facilitate the uncertainty modelling and analysis, all of those uncertainties are shifted to the load indicator of the corresponding sequencing control. Case studies are presented to show that using the proposed method of uncertainty shift and modelling the impacts of the uncertainties on the sequencing controls can be easily identified and analysed. [ABSTRACT FROM AUTHOR]

The economic and environmental benefits of building retrofits have been acknowledged. However, there is a series of barriers that threaten to impede implementing successful retrofit projects such as: lack of funding, lack of interoperability, and unstructured decision making. This paper aims to address these barriers by providing the framework of the Virtual Retrofit Model (VRM), an affordable computational platform that supports streamlined decision making for building retrofit projects. An occupant survey was implemented to identify the primary requirements and perceptions from different types of stakeholders of the buildings. The responses were analyzed to identify the most important criteria of the future retrofit projects to focus on if it were to be renovated in the future. A case study approach was used to describe the outcomes from a year-long demonstration project that has been conducted at an aging commercial building. The research activities focused on integrating theories and technologies of Building Information Modeling (BIM), energy simulation, agent-based modeling, multi-criteria decision support system, and software application that can be employed and adopted in building retrofit projects. The software prototype is designed to connect buildings to a smart grid environment where building energy data should be shared for intelligent decision making. [ABSTRACT FROM AUTHOR]

[Display omitted] • A comprehensive overview of NZEBs is conducted by using bibliometric techniques. • Hot research topics of NZEBs are identified through keyword co-occurrence analysis. • The research trend, key journals, publications, authors, and countries are analyzed by co-citation analysis. • The thematic evolutionary analysis of NZEBs was investigated using SciMAT tool. This study aims to provide a comprehensive overview of three-decade research developments in the field of Net-Zero Energy Buildings (NZEBs) by using bibliometric analysis techniques. The specific objectives of this paper are to: (1) identify the most influential researchers, sources, and countries in this field; (2) identify the main thematic research focus areas and hot topics in the field; and (3) analyze the thematic evolution of NZEBs. VOSviewer software was used to realize the first two objectives while to meet the third objective, the SciMAT tool was employed to examine the thematic evolution of the field within three sub-periods (1995–2005; 2006–2015; 2016–2022). The results indicate that the main thematic areas of the field were initially limited to a few themes including 'environmental impact', 'green buildings', 'building materials', and 'fuel cells', and it expanded in the next periods by encompassing more themes. The results also showed that the field has now reached a certain level of consolidation by acknowledging the significance of three major themes: (1) buildings' energy conservation, (2) energy production and energy storage, and (3) methodological approach. Nevertheless, the evolutionary analysis points out that the field is still expanding with several themes newly emerged such as 'electric vehicles', 'zero-emission neighborhood' and 'smart buildings'. The findings offer a status-quo overview and critiques of research developments in NZEBs as well as emerging themes in the field that should be investigated by future studies. [ABSTRACT FROM AUTHOR]

• Nonuniformity factors of thermal resistance and capacitance are proposed. • The transmission load is reduced by ∼35% after optimizing nonuniformity factors. • Optimal nonuniformity factors of resistance/capacitance are 0.8 and 0.6 respectively. To increase the energy efficiency of buildings and lower carbon emissions, heat losses through exterior walls could be considerably decreased by using high-performance thermal insulation materials with superlow thermal conductivities or superhigh heat capacities. A superinsulating layer would result in a highly nonuniform distribution of thermal resistance and capacitance within the exterior wall. An interesting problem concerns how to engineer the nonuniform thermal distribution for given total resistances and capacitances to improve energy efficiency. This paper proposes using a thermal nonuniformity factor to quantitatively characterize the nonuniform distribution of thermal resistance and capacitance in an exterior wall built with superthermal aerogel insulating panels and given total resistance and capacitance values. Various thermal nonuniformity factors for several insulation modes were investigated by calculating the relevant transmission loads through the walls. The effect of the thermal nonuniformity factor on the wall transmission load was explored to improve the energy efficiency of a building. The results indicated that the wall heat flux could be greatly decreased by engineering walls with a high nonuniformity factor. For instance, engineering a wall in a sandwich mode with thermal nonuniformity factor of 0.97 could decrease the wall heat flux by ∼35% compared to a uniform wall. These findings on engineering walls based on thermal nonuniformity factors to decrease the wall heat flux could be a useful reference for superinsulated buildings and for further improving thermal insulating performance and building energy efficiency. [ABSTRACT FROM AUTHOR]

Abstract: Hainan province is the only tropical and island province in China, with the unique climatic and geographical features. It has a different energy performance in public buildings from those in the inland areas. This paper investigated the current situation of energy consumption of the sampled public buildings, including 26 hotels, 24 office buildings and 7 department stores, through analyzing utility billing data, on-site survey and measurement, and site operational records. The breakdowns of energy consumption found out that the electricity dominated the energy consumption for all the sampled buildings and the air conditioning system played a key role in the energy consumption of major building services. By analyzing the main factors for building energy consumption, the annual average energy use indexes were calculated to be 141.63kWh/m2, 87.70kWh/m2, and 166.95kWh/m2 for hotels, office buildings and department stores, respectively. Besides, the comparison between Hainan province and other areas revealed that the building energy consumption of this tropical province was at a low level. Because indoor environment quality can influence the health and work efficiency of humans in buildings, field tests on the indoor environment quality were also carried out, and the performance of indoor environment quality was rated. [Copyright &y& Elsevier]

Abstract: Carbon emissions associated with new buildings are being reduced by the increasing stringency of building regulations . It is however, the retrofitting of existing buildings for minimum carbon emissions throughout their expected lifetime that poses the biggest challenge at this time. This paper considers the assessment of retrofitting non-domestic buildings using two different modelling techniques, Static Simulation Modelling and Dynamic Simulation Modelling. A case-study building, located on the campus of University College Cork, was used for the modelling and actual retrofit. This allowed for a comparison to be made between actual measured data and the proposed modelling techniques with regard to thermal energy modelling. The measured data along with the two types of modelled data, gave an extensive data set from which the assessment of the two modelling techniques could be made. This assessment proposes a modelling technique which is sufficient to base future retrofitting decisions upon. This research demonstrates clearly that fundamental building information, simplified occupancy profiles, simplified weather data and a less time consuming model is a sufficient and adequate means to base retrofitting decisions on. [Copyright &y& Elsevier]

Abstract: This paper presents a top-down strategy for detecting faulty HVAC units across different levels. Energy flow models of HVAC units are developed while the energy is the main feature extracted from the vast amount of real-time data of the HVAC system in a building. A temporal and spatial partition strategy for analyzing HVAC units is proposed. The partition takes into account of architectural, environmental, and human factors. The partition strategy leads to more appropriate thresholds for fault detection. Numerical examples are presented to demonstrate the detection of a systemwide fault and a fault at the VAV level. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to investigate the reasons for ever growing energy consumption in buildings and to give enhanced supervision strategies for reduction of building energy consumption compared to regular ones. A case study of Ningbo city was used to make a detail analysis. Several factors were determined as the reasons for ever growing building energy consumption including climate change, household electricity load increase, the growth of real estate, fast-growing household electrical appliances, high energy consumption in existing buildings, changes in industrial structure and the lack of enhanced government supervision. Then the discussion of suitable countermeasures shows that only enhanced supervision strategies are currently applicable. Finally, it is concluded that enhanced government supervision strategies, including the establishment of a strict control system for new built buildings through information integration and encryption, establishing an energy efficiency supervision system of large-scale public building and a carrot-and-stick approach with added expert checklist for the building application of renewable energy, showed great advantages in promoting building energy efficiency in Ningbo, compared with other cities. These supervision strategies are applicable in other cities as they are in the similar situations in the enforcement of building energy efficiency. [Copyright &y& Elsevier]

Abstract: In this paper, in contrast to the usual rough estimation, we present a model to simulate and evaluate the direct, indirect economic and environmental impacts of the implementation of building energy efficiency standards on Chinese economic system and environment by 12 indicators in two scenarios. Four indicators are used to evaluate the direct economic impact degree, five indicators are used to evaluate the direct environmental impact degree, three indicators are used to evaluate the indirect economic impact degree of 34 sectors and the whole Chinese economic system. This research makes it possible to link developments in the implementation of building energy efficiency standards with environmental and economic structure change. The most important finding is that the implementation of building energy efficiency standards can reduce a large amount of pollutants emissions and increase the GDP at the same time. [Copyright &y& Elsevier]

[Display omitted] • A direct expansion air handling unit assisted by liquid desiccant is proposed. • Temperature and concentration of solution are adjusted by heat pump and water addition. • The air handling unit can work for various sensible and latent heat ratios. • The system COP increases 37% in summer and 11% in winter compared with normal system. • The system shows better economic performance than conventional systems. Reheating is needed in the traditional cooling and dehumidification system, which results in the offset of cooling and heating. There is no reheating in the liquid desiccant system, but equipment for latent heat and sensible heat are both necessary. This paper proposes a direct expansion air handling unit assisted by liquid desiccant (LDAHU) for adapting to various sensible and latent heat ratios using only one set of equipment. Numerical models were developed for calculating the system performance under various load demands of an office building in Nanjing, China. A comparison of the energy performance and economy between the LDAHU system and two conventional systems, i.e., an electric chiller and boiler (ECB) system and an air source heat pump (ASHP) system, was performed. The results indicated that the LDAHU system can treat the mixed air for achieving the required state of the supply air directly by controlling the temperature and concentration of the sprayed solution and can operate efficiently in both summer and winter. Compared with the ECB system and the ASHP system, the coefficient of performance of the LDAHU system is 13.2% and 30.4% larger, respectively, in summer, and 37.0% and 6.2% larger, respectively, in winter. The total cost savings for the LDAHU system over 15 years of life-cycle operation are 11.6% and 6.8%, respectively, although the initial costs are 5.1% and 31.2% higher, respectively, resulting in the dynamic payback periods as 0.9 and 5.3 years, respectively. [ABSTRACT FROM AUTHOR]

• A kind of fiber felt/silica aerogel thermal-insulation material was prepared. • The microstructure and hydrophobic properties were characterized. • The moisture absorption rate and water-vapor transmission rate were measured. • The thermal conductivity under different humidity conditions was measured. Building thermal insulation can enhance the thermal environment of buildings, thus decreasing energy consumption. Traditional inorganic insulation materials are easy to absorb moisture so that their insulation performance is reduced significantly when they are used in humid environment. It is of great significance to develop new building insulation materials with low moisture absorption and high thermal insulation. In this paper, sol-gel method and ambient pressure drying were used to prepare polyester fiber felt/silica aerogel composite thermal-insulation material. The hydrophobic properties and the microstructure of the aerogel composites were characterized. Moreover, the water-vapor transmission rate and the moisture absorption rate of the aerogel composites and the fiber felt in various humidity conditions were measured. Transient thermal plane method was applied to the measurement of the thermal conductivity. The thermal conductivity and moisture absorptivity of the polyester fiber felt increased significantly with the increase of ambient humidity. After being composited with silica aerogel, both moisture absorptivity and thermal conductivity decreased significantly and kept small value even in high humidity. The moisture absorption rate of the composites is approximately 1%, and the thermal conductivity is 0.0176–0.0187 W·m−1·K−1. Therefore, the prepared fiber felt/silica aerogel composites could be widely used for building insulation even in humid areas. [ABSTRACT FROM AUTHOR]

• Analysis of the relationship between zoning, internal gains and comfort band. • Steady-state analyses on a two-zone building showing when zoning is not effective. • Dynamic simulations on a residential archetype. • Assessment of the impact of zoning assumptions on energy rating system. Thermal zoning is a commonly adopted building energy efficiency initiative, since thermally segregating conditioned spaces is generally expected to minimise energy losses when conditioning unoccupied spaces. When comparing a partitioned building with widely differing heat gains between zones to an equivalent non-partitioned building, 'zoning' might not always beneficial. This paper analyses the fundamental thermal processes involved in these scenarios by firstly undertaking a number of steady state analyses, demonstrating that there are scenarios where the thermal energy required to maintain comfort conditions is less for an open-plan arrangement than for a more highly partitioned building. We then performed dynamical simulations of a simple building, confirming the steady state analyses and showing that, for space heating, connecting the spaces can significantly reduce the energy demand. It was concluded that whenever two zones are both conditioned to the same set-points, thermally connecting zones always leads to an energy demand lower or equal to thermally isolated zones. We then conducted simulations of an archetypal residential building with intermittent conditioning of spaces. The results showed that thermally connecting the spaces can be beneficial in climates from cool to warm temperate, with a decrease in energy demand from 2.2 to 9.9%, while this was not beneficial in a hot and humid climate, with an energy demand increase of 0.2 to 2.3% for the thermally connected scenario. [ABSTRACT FROM AUTHOR]

• Evaluation of existing approaches or projects of building archetype. • Developing a generalized multi-scale building archetype methodology. • Implementing the data-driven approaches for the characterization of building archetypes. • Comparison of the modeling results of multi-scale building archetype at BEM and UBEM levels. Globally the building sector accounts for a significant portion of the overall energy demand and greenhouse gas emissions of any country. The most common approach for the collection of modeling and benchmarking data that can be used for predictions of energy performance at a national or urban scale is through classification of the building stock into representative archetypes. Developing such building archetypes is a complex task due to the difficulties associated with gathering detailed geometric and non-geometric data at an urban scale. Although existing databases and projects provide a valuable overview of a building stock, the information about buildings' physical descriptions are not regularly updated. Moreover, these databases cover only the national top-level archetypes and lack crucial information related to city or district scale building stocks. The use of national scale archetypes requires many assumptions that may not hold true for energy modeling at urban or district scale. This paper proposes a multi-scale (national, city, county and district) archetype development methodology using different data-driven approaches. The methodology consists of following five steps: 1) data collection, 2) segmentation, 3) characterization, 4) quantification, and 5) modeling results. We developed a test case based on the available building stock data of Ireland. The test case used previously developed archetype geometries coupled with the parameters determined by the characterization process to calculate annual energy use (kWh) of buildings at a multiple-scales. The resulting archetypes at national, city, county and district scale are analyzed and compared against one another. The results indicate that significant differences occur in terms of energy modeling results when national scale archetypes are used to simulate the energy performance of buildings at the local scale. These multi-scale building archetypes will aid local authorities and city planners when analyzing energy efficiency and consequently, help to improve sustainable energy policy decisions. [ABSTRACT FROM AUTHOR]