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About 40% of global energy consumption is due to buildings. For this reason, many countries have established strict limits with regard to building energy performance. In fact, the minimization of energy consumption and related polluting emissions is undertaken in the public perspective with the main aim of fighting climate change. On the other hand, it is crucial to achieve financial benefits and proper levels of thermal comfort, which are the principal aims of the private perspective. In this paper, a multi-objective multi-stage approach is proposed to optimize building energy design by addressing the aforementioned public and private aims. The first stage implements a genetic algorithm by coupling MATLAB® and EnergyPlus pursuing the minimization of energy demands for space conditioning and of discomfort hours. In the second stage, a smart exhaustive sampling is conducted under MATLAB® environment with the aim of finding constrained cost-optimal solutions that ensure a drastic reduction of global costs as well as of greenhouse gas (GHG) emissions. Furthermore, the impact of such solutions on heat emissions into the external environment is investigated because these emissions highly affect urban overheating, external human comfort and the livability of our cities. The main novelty of this approach is the possibility to properly conjugate the public perspective (minimization of GHG emissions) and the private one (minimization of global costs). The focus on the reduction of heat emissions, in addition to the assessment of energy demands and GHG emissions, is novel too for investigations concerning building energy efficiency. The approach is applied to optimize the retrofit of a reference building related to the Italian office stock of the 1970s. [ABSTRACT FROM AUTHOR]

Buildings consume a significant amount of energy, estimated at about one-third of total primary energy resources. Building energy efficiency has turned out to be a major issue in limiting the increasing energy demands of the sector. Literature shows that building user behavior can increase the efficiency of the energy used in the building and different strategies have been tested to address and support this issue. These strategies often combine the quantification of energy savings and qualitative interpretation of occupant behavior in order to foster energy efficiency. Strategies that influence building occupant behaviors include eco-feedback, social interaction, and gamification. This review paper presents a study conducted on the state of the art related to the impact of building user behavior on energy efficiency, in order to provide the research community with a better understanding and up-to-date knowledge of energy, comfort-related practices, and potential research opportunities. Achieving and maintaining energy-efficient behavior without decreasing the comfort of building occupants still represents a challenge, despite emerging technologies and strategies as well as general research progress made over the last decade. Conclusions highlight eco-feedback as an effective way to influence behavior, and gamification as a new opportunity to trigger behavioral change. The impact of user behavior is difficult to quantify for methodological reasons. Factors influencing human behavior are numerous and varied. Multi-disciplinary approaches are needed to provide new insights into the inner dynamic nature of occupant’s energy behavior. [ABSTRACT FROM AUTHOR]

Climate change, driven by greenhouse gas emissions, is a growing global concern, threatening world-wide environment, health and economy. Energy needs for buildings are a large source of greenhouse gas emissions. As the energy needs of buildings strongly depends on weather patterns, this paper investigates how climate change may impact building heating and cooling loads, cost-optimal efficiency measures, and renewable energy production. Eight locations (Stockholm, Milan, Vienna, Madrid, Paris, Munich, Lisbon, and Rome) highlight differences among European climates. Weather datasets, commonly used in building energy simulations, are evaluated to see how climatic parameters have changed over recent decades. A future climate change scenario (with uncertainties) is analyzed for the year 2060. Weather files are used to drive building energy simulations for a standard baseline and a (Nearly Zero Energy Building) NZEB residential building whose design is improved using a cost-optimization approach. The analysis indicates most currently available weather datasets cannot assure reliable results with building simulations. We find the energy balance in European buildings will significantly change under future conditions: heating will decrease by 38%–57%, while cooling will increase by +99%–380% depending on location. In future NZEBs, efficiency measures to reduce cooling needs and overheating will be favored (e.g. roof insulation, window type, solar shading, envelope finishes), illustrating how improving energy efficiency will be more crucial within climate change scenarios. Compared to the baseline, more efficient NZEBs will enable renewable energy to much better cover building needs. There will also be advantages from reducing winter and summer peak demand, particularly when coupled to short-term electrical storage. When solar resource is limited in winter, more airtight, better-insulated NZEBs improve PV self-consumption. • Climate change impact is studied in baseline and NZEBs in different climates. • Different weather datasets and a climate change scenario are simulated. • Future heating loads decrease by 38%–57%, cooling increase by +99%–380%. • Energy efficiency measures to reduce cooling and overheating are favored. • NZEBs enable renewable energy to much better cover building needs. [ABSTRACT FROM AUTHOR]

Abstract: Although there have been multiple studies on window thermal properties in energy-efficient fenestration design, the effects of building geometry factors have been studied much less. The study presented in this paper evaluated the role of geometry factors, such as window orientation, window to wall ratio, and room width to depth ratio, on building energy performance in a commercial office building. A series of energy simulations were performed for six climate zones in the United States using a model of a room in a typical office building created in Design Builder, an energy analysis program, to evaluate total annual energy consumption. The simulation results were analyzed to find the combination of parameters yielding the lowest energy consumption and to define potential energy savings for office buildings located in various climate zones. The study showed that geometry factors affect energy consumption significantly in hot climates (Zones 2 and 3) and cold climates (Zones 6 and 7) but only marginally in temperate climates (Zones 4 and 5). Energy savings were on average 3% and 6% reaching a maximum of 10% and 14% in hot climates (Zones 2 and 3) and 1% in temperate and cold climates (Zones 4–7). [Copyright &y& Elsevier]

Abstract: This paper presents a simplified energy performance assessment method for existing buildings in cooling season, which is based on energy bill disaggregation and energy performance analysis. This method requests very limited building energy data and can effectively assess the energy performance at building and system levels, and disaggregate the whole-building consumption into consumptions of three groups of end-uses. This method is based on two basic energy balance principles, i.e., the electricity consumption balances at building level and the cooling energy balances between demand side and supply side of HVAC systems. An optimization algorithm is developed to establish best possible cooling energy balances by minimizing the balance residuals and therefore to disaggregate the energy consumption of different users. The performance of the proposed method was validated in two existing buildings in Hong Kong and Beijing. The results, including the disaggregated energy consumptions and the key energy performance indicators of HVAC system, agreed well with the “measured” data in mechanical cooling months. [Copyright &y& Elsevier]

Abstract: The paper presents a methodology that has been developed and implemented for the simulation of electricity use (kWh) as well as electricity pricing (c€/kWh) in buildings. In the simulation, electricity pricing has been treated as dependent on electricity use as energy pricing tariffs usually discriminate between specific electricity consumption zones, time periods, etc. Pricing and electricity use, together, may then provide an estimation of the building electric energy cost (€). The simulation runs on an hourly resolution allowing to highlight some potential wasteful and costly practices in energy use and management and providing insight to user behavior, which has been a key driver for its development. The key aspects of the simulation approach are presented and its use is demonstrated in two different buildings in Greece and Italy. The methodology is now adapted to allow for neural network based, real time training, which will also be briefly introduced. [Copyright &y& Elsevier]

Abstract: Apparent window size contradictions arise when optimizing simultaneously for low energy (small sizes) and visual comfort (large sizes). Diverse multi-objective optimization methods exist, but basic questions must be solved beforehand such as choosing appropriate evaluation measures. This work aims to determine the suitability of combined optimization criteria on window sizing procedures for low energy consumption with high visual comfort and performance. The paper showcases diverse measures available to valorise energy consumption and visual aspects. A series of energy and visual criteria were selected, defining acceptance thresholds for dynamic evaluations. Whole-building computer simulations were performed on a standardized office located in a temperate climate. Discrete window-to-wall ratio variations were studied to demonstrate how these criteria affect the solution space. Results were classified using a graphical optimization method, obtaining a solution space satisfying both energy and visual requirements. Most project expectations can be met within the range of sizes. However, unprotected windows barely meet acceptance criteria, needing additional control devices. Applying various related criteria with adequate values increases the diversity of acceptable solutions but too many limits it. Clear objectives and acceptance ranges have to be conceptualized in order to translate them into decisions. This becomes important when involving team design. [Copyright &y& Elsevier]

The characteristics of urbanism and its construction properties are critical factors in shaping energy demand and citizens' quality of life. Urban typology approaches classify the urban built environment through common patterns, facilitating building energy analysis and energy retrofitting. However, current methods struggle between detailed local building classifications and broad land spatial distributions, which include built environments. This paper presents a systematic methodology to objectively identify and classify residential urban areas according to representative Homogeneous Urban Zones (HUZs), aiming at improving the analysis of urban block energy behaviour by considering buildings and their surrounding urban conditions. Based on the characterisation of urban clusters, this methodology merges current urban classifications and defines representative urban block indicators using geographic information systems. The urban clusters are statistically delimited by testing a set of urban indicators grouped by constructive systems, morphology, and surface cover. If they successfully meet the statistical criteria of the outlier's filter, test-of-goodness-fit, and non-overlapping check, the clusters are then validated as HUZs; otherwise, they are merged, split, or rejected. Finally, the urban residential areas classified as HUZs are characterised by average, maximum, and minimum range values based on a set of representative urban indicators. This methodology was applied to the city of Madrid resulting into the definition of 10 statistically significant HUZs, grouped into four different categories. Because of the shown good adaptability of HUZs to the local urban context, it is expected that the present HUZ method represents an improvement for the standardisation of the residential building energy modelling evaluation. • A method to systematically classify Homogeneous Urban Zones (HUZs) is presented. • HUZs discern urban zones relevant for the residential buildings' energy evaluation. • The urban block scale of HUZs considers local urban and building characteristics. • HUZs are delimited statistically throughout 10 urban block indicators. • 10 statistically significant HUZs were identified in Madrid. [ABSTRACT FROM AUTHOR]

Abstract: The main requirements of European Directive 2002/91/EC on the energy performance of buildings (EPBD) are the application of minimum energy performance requirements for new buildings and certification of the energy performance of buildings. Its transposition into the national law of member states of the European Union has signified the appearance of new and more onerous requirements in terms of construction quality from the point of view of energy performance, and in terms of the procedure for certification of the energy performance of buildings. In both cases, the levels required tend to be based on climatic conditions, fundamentally in countries such as Spain, where the climatic variability is very pronounced. This paper presents a methodology developed for the climatic zoning of the localities not included in the above-mentioned regulations in order to facilitate their application. By way of example, the method is shown which was used to generate the climatic files and carry out the subsequent climatic zoning of all the municipalities of the region of Andalusia in southern Spain. [Copyright &y& Elsevier]

Abstract: With the development of modern computer technology, a large amount of building energy simulation tools is available in the market. When choosing which simulation tool to use in a project, the user must consider the tool''s accuracy and reliability, considering the building information they have at hand, which will serve as input for the tool. This paper presents an approach towards assessing building performance simulation results to actual measurements, using artificial neural networks (ANN) for predicting building energy performance. Training and testing of the ANN were carried out with energy consumption data acquired for 1 week in the case building called the Solar House. The predicted results show a good fitness with the mathematical model with a mean absolute error of 0.9%. Moreover, four building simulation tools were selected in this study in order to compare their results with the ANN predicted energy consumption: Energy_10, Green Building Studio web tool, eQuest and EnergyPlus. The results showed that the more detailed simulation tools have the best simulation performance in terms of heating and cooling electricity consumption within 3% of mean absolute error. [Copyright &y& Elsevier]

Buildings account for an important share of global energy consumption and CO2 emissions, so increasing energy efficiency in buildings is essential to ensure an energy transition and sustainable development. In this study, we evaluate the energy benefits obtained through the application of a double-skin façade (passive façade). Subsequently, this is combined first with an airflow network and a control logic for opening windows (active façade) and secondly with transparent photovoltaic modules. The proposed measures are applied to an office building at the University of Naples Federico II. Building's thermal and energy performance are evaluated using EnergyPlus software, starting from validated energy demands of the base buildings. Both passive and active façade provided a reduction in primary energy consumption for space conditioning, of about 17% and 28%, respectively, and in the total primary energy consumption, of about 4% and 9%, respectively. The best solution, with the maximum energy saving in total primary energy consumption, approximately 20%, is achieved with the active façade and with 80% of the outer layer of the double-skin façade covered by PV modules. The results show that transparent double-skin facades are promising and effective for energy retrofit. [ABSTRACT FROM AUTHOR]

Many efforts have been detected to investigate thermochromic (TC) glazing for improving building energy saving, while only a few approaches for daylight performance analysis. In this study, the performance of TC glazing is investigated based on multi-objective optimization for minimizing energy demand while maximizing daylight availability. The effects of five parameters including transition temperature, solar transmittance in clear state, solar transmittance modulation ability, luminous transmittance in clear state, and luminous modulation ability on the building energy consumption and useful daylighting illuminance (UDI300–3000) are examined. Linear Programming Technique for Multi-dimensional Analysis of Preference (LINMAP) is used for the decision-making of Pareto frontier. This research aims to explore the ideal thermochromic glazing by considering the daylight and energy performance of a typical office room, taking the weather condition of Xiamen, China as an example. Although it is impossible to achieve both optimal values of energy consumption and UDI300–3000 simultaneously, the proposed multi-objective optimization method could still provide low energy consumption with sufficient daylight. The non-dominated sorting of Pareto optimal solution (POS) demonstrated that the optimum building energy consumption and UDI300–3000 for single glazed windows are 46.64 kWh/m2 and 70.92%, respectively, while the values for double glazed windows are 44.40 kWh/m2 and 71.88%, respectively. The selected hypothetical TC windows can improve the building energy and daylighting performance simultaneously when compared with traditional clear single and double glazed windows. The presented framework provides a multi-objective optimization method to determine the most suitable TC glazing technologies for designers and architects during the design and retrofit procedure. [ABSTRACT FROM AUTHOR]

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Energy performance of buildings is an important issue to estimate the energy waste of buildings and their impact on the environment, so designing energy-efficient buildings could improve their energy performance. In this case, the estimation of heating and cooling loads plays an important role in this regard. However, there are few factors with unpredictable influences on the heating and cooling loads. This study has attempted to analyze the eight parameters that can significantly affect the heating and cooling loads through the Neuro-fuzzy logic approach. Accordingly, the eight parameters of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution were considered as inputs and predicting the cooling and heating load changes was regarded as the output of this study. The model was developed and its results were measured in two regression indicators of r and RMSE. Based on the obtained results it was found that roof has the strongest impact on heating and cooling loads (RMSE: 4.3596), moreover, if two factors were concurrently changed, then the combination of relative compactness and wall area can significantly affect the heating and cooling loads (RMSE: 2.6312). The most influential combination of three factors is observed as well for the heating and cooling load and these factors are relative compactness, wall area and glazing area (0.5948). The most influential combination of three factors is observed as well for the hearing load and these factors are relative compactness, wall area and glazing area (RMSE: 0.5948 and RMSE: 1.5769 for heating and cooling load, respectively). However, Neuro-fuzzy logic showed overfitting for more than two inputs, therefore it is not recommended for more than two inputs. [ABSTRACT FROM AUTHOR]

A current global effort is being made in order to find solutions that will limit global warming. The building sector is responsible for the highest share of total energy consumption, which requires the continuous development of strategies to increase buildings energy efficiency. Considering the high share of emissions generated by buildings, improving buildings energy performance can significantly slow down global warming. A key point in developing strategies to reduce energy consumption and making more accurate forecasts of the evolution of global warming is to determine the actual energy consumption generated by the use of buildings, which in some cases may differ from the theoretical one. In order to determine the actual energy consumption, with high accuracy, determinations in situ of the thermal resistance of the building elements that form the envelope, are essential. The article presents the main methods for determining the thermal resistance of building elements in situ, factors influencing the accuracy of the results, and recommendations for the use of methods to reduce the risk of errors. [ABSTRACT FROM AUTHOR]

The building sector has a high level of energy consumption caused mainly by the buildings heating and cooling energy demands to satisfy indoor comfort requirements. Reducing both the amount of energy consumed and the life cycle cost is a main challenge for the construction of buildings. It is evident that sustainable materials have low environmental impacts and need low consumption of energetic resources in addition to their durability and recyclability. Therefore, this research aims to test different sustainable materials available in Egypt for the construction of building envelopes that include local stones "Marble and Limestone" and insulation materials "Polyurethane-expanded and Extruded polystyrene (XPS) foam" in order to achieve savings in energy and total life cycle cost. The simulation tests were conducted through Design Builder software. The results aim to provide solutions for building designers to achieve energy-efficiency and costeffective design. The proposed alternatives showed a significant reduction in energy consumption by up to 62% and the total life cycle costs significantly reduced by up to 45.8%. [ABSTRACT FROM AUTHOR]

The heat transfer coefficient (HTC) is a very important factor influencing the energy performance of a building. Recent studies have shown the importance of on-site measurements of the HTC in reducing the performance gap in buildings. However, its measurement setup and calculation procedures are known to be intense and complex. Due to this, many stakeholders in the building industry find it impractical and insufficient for their needs. This paper presents the results of an international survey that targets such stakeholders with the aim to get their perspectives on HTC measurements on-site. Several stakeholders from 14 countries in Europe participated in the survey. The survey is categorized into four parts: a) basic data about the participants, b) their interest in methods for measured energy performance, c) their views on the characteristics of such a methodology and d) their concerns and opportunities. The results reveal that the stakeholders are highly interested in measuring the HTC on-site. The results also provide interesting insights on the aspects relevant for them and their customers. In particular, we elaborate on their perspective on the time to conduct the measurement, the cost of the setup, the measurement duration and the acceptable error. The assimilated understanding from the survey will help the building and the construction industry to identify opportunities for a progressive assessment campaign involving on-site measurements. This study is part of the International Energy Agency's Energy in Buildings and Communities Programme (IEA EBC) Annex-71 project titled 'Building energy performance assessment based on optimized in-situ measurements'. • Survey on HTC measurements involving 13 types of stakeholders from 14 countries. • Their perspectives on duration, cost and accuracy of the HTC test are presented. • Their perspectives on the concerns and further opportunities are discussed. • Their preferences are compared with existing HTC measurement methods. • All stakeholders are highly interested in measuring the HTC on-site. [ABSTRACT FROM AUTHOR]

Cross-domain information is essential for building energy performance assessment. The heterogeneous nature of this information is a major source for inefficient assessments. The semantic web provides a flexible pathway for addressing recognised interoperability issues. However, further implicit knowledge in cross-domain information could provide meaningful solutions for such assessments. This paper aims to develop a conceptual framework that links cross-domain information, infers implicit knowledge, and empowers building managers with insightful assessments. The framework integrates Web Ontology Language (OWL) ontologies, Resource Description Framework (RDF) instances, and a set of predefined rules to infer implicit knowledge, which can satisfy data requirements of performance metrics and enable meaningful performance assessments. Then building managers can identify inefficient building operations and improve energy efficiency while maintaining desired building functions. This approach reduces burdensome intervention from the managers when compared with traditional solutions. A demonstration highlights the engineering value by evaluating energy performance of a university building. • The work links cross-domain information for building energy performance assessment. • Semantic reasoning is employed for inferring implicit knowledge in this information • Inference rules are defined to satisfy data requirements of key performance metrics. • Results indicate the work provides comprehensive assessments for building managers. [ABSTRACT FROM AUTHOR]

Non-domestic buildings contribute 20% of the UK's annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7% is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retrofitting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retrofit packages can be very challenging. The need for effective decision-making has led to the wide adoption of artificial intelligence in the retrofit strategy design process. However, the vast retrofit solution space and high time-complexity of energy simulations inhibit artificial intelligence's application. This paper presents an energy performance prediction model for non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic buildings energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provide a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space. The model is evaluated by assessing three thousand retrofit variations of a case study building, achieving a root mean square error of 1.02 k g C O 2 ∕ m 2 × y e a r equal to 1.7% of error. • Addressed impracticality of optimisation methods in supporting deep energy retrofit. • A machine learning model to calculate non-domestic buildings' energy performance. • Identified essential features of non-domestic buildings affecting energy performance. • TOptimised the model by tuning hyperparameters using an evolutionary algorithm. • Evaluated model performance by scoring thousands of variations in a sample building. [ABSTRACT FROM AUTHOR]

• Occupant behavior is a main factor influencing the building energy performance. • Synthetic review about the occupant presence and occupants' interactions was presented. • Monitoring approach, occupant modeling and energy feedback programs are the key approaches for human behavior analysis. • The integration of social science is essential for the analysis process of the occupant behavior topic. The building sector in the world is responsible for nearly 36% of the final energy consumption and nearly 40% of total direct and indirect CO 2 emissions. Reducing buildings energy consumption is an absolute necessity, and one of the most efficient methods is the estimation and optimization of building energy performance. One of the significant factors affecting the building energy performance is the occupant behavior. Recently, there has been growing interest in the occupant behavior field. Within the framework of this context, different methods have been proposed and developed especially when the traditional methods, such as interview and survey, are not efficient and sufficient to analyze and to predict the occupant behavior with better accuracy. This research provides a comprehensive characterization of the occupant behavior through, a review of current approaches for occupant behavior analysis. The main key issues and drivers affecting the energy behavior of habitants identified on the literature were synthetized and presented. Furthermore, the paper highlights the necessity of including the energy feedback programs as a main key in the topic of occupants, in order to integrate them in the building performance process. This research is a part of a European project H2020 "Holistic Energy and Architectural Retrofit Toolkit (HEART)". [ABSTRACT FROM AUTHOR]

• A systematic performance-based approach is developed to generate TMY weather files. • Machine learning is used to assign weighting factors to generate weather files. • Comparing five different training approaches in terms of heating and cooling modes. • The approach is applied to a small office building set in two climatic locations. • The results demonstrated an 18% RMSE reduction in predicting energy performance. A typical meteorological year weather file, supposedly representing the climatic conditions, is commonly used as an input for building energy simulation to predict the long-term performance of the buildings. Depending on the building type and climatic locations, the deviation between the actual long-term average energy performance and the predicted one may not be insignificant. Weather files are synthetically constructed on historical weather data over a long period of time for an array of weather parameters, such as solar radiation, temperature, wind speed and others. The statistical procedure to construct the weather files depends on the weights assigned to these weather parameters. Under current practice, these weighting factors are universally assigned regardless of climatic locations nor building applications based on experts' judgment. The objective of this paper is to propose a novel approach in assigning weighting factors systematically, demonstrate its applicability, and quantify the potential reduction in deviation. Instead of basing on experts' judgement, Random Forest algorithm was deployed to extract the feature importance of the weather parameters in order to assign weighting factors straightly proportional to their impacts on energy performance of buildings. To ensure the assigned weighting factors fully reflect the climatic characteristics, five different training approaches were proposed, and the resulting climatic location dependent weather files were constructed and cross-validated. The newly constructed typical meteorological year weather files were applied to two different climatic locations to investigate the representativeness of these new weather files as compared to existing weather files and historical weather data of actual years. The representativeness was indicated in terms of the deviation in predicted energy performance of buildings between using the typical meteorological year weather file and actual historical weather data. The results indicated that typical meteorological year weather file based on the novel approach offers better prediction (with statistical significance) on energy performance for climatic locations with wider temperature range. As a result, the suggested approach avoids potential under/oversizing of equipment and promotes energy conservation. [ABSTRACT FROM AUTHOR]

The gap between the designed and measured building energy consumption is one of the biggest obstacles to the realization of energy conservation goal. The factor analysis method has been typically used in previous studies to explore the individual factors affecting building energy performance gap, while few studies examined the complex interrelationships among the influencing factors. In this research, the interpretive structural modelling approach was developed and applied to identify the key factors and explore the interrelationships among the factors affecting building energy performance gap. This paper first identified 47 factors by reviewing 276 relevant articles. Sixteen representative factors were further identified by interviewing 12 interviewees with 7–25 years' practical experience on building energy management. Second, a six-level hierarchical structure was developed based on interpretive structural modelling to illustrate the intricate interrelationships among the representative factors. Third, "Matrice d'Impacts croises-multipication applique a classement" was applied to demonstrate the driving force and dependence power of the representative factors. Four clusters of factors were identified, namely dependent factors, driving factors, autonomous factors and linkage factors. Fifth and finally, 10 strategies were proposed to address these factors and their interrelationships. The findings of this research will help not only researchers in understanding the factors and their hierarchical structures affecting BEPG but also policymakers and practitioners in understanding the priority of allocating limited resources to address these factors and issues. • Sixteen representative factors affecting building energy performance are identified. • Hierarchical structure of the representative factors is developed. • Ten strategies are proposed to improve building energy performance. • Interpretive Structural Modelling method is developed and applied. [ABSTRACT FROM AUTHOR]

The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

The energy performance prediction of buildings plays a significant role in the design phases. Theoretical analysis and statistical analysis are typically carried out to predict energy consumption. However, due to the complexity of the building characteristics, precise energy performance can hardly be predicted in the early design stage. This study considers both building information modeling (BIM) and statistical approaches, including several regression models for the prediction purpose. This paper also highlights a number of findings of energy modeling related to building energy performance simulation software, particularly Autodesk Green Building Studio. In this research, the geometric models were created using Autodesk Revit. Based on the energy simulation conducted by Autodesk Green Building Studio (GBS), the energy properties of five prototype and case study models were determined. The GBS simulation was carried out using DOE 2.2 engine. Eight parameters were used in BIM, including building type, location, building area, analysis year, floor-to-ceiling height, floor construction, wall construction, and ceiling construction. The Monte Carlo simulation method was performed to predict precise energy consumption. Among the regression models developed, the single variable linear regression models appear to have high accuracy. Although there exist some limitations in applying the equation in EUI prediction, the rough estimation of energy use was realized. Regression model validation was carried out using the model from the case study and Monte Carlo simulation results. A total of 35 runs of validation were performed, and most differences were maintained within 5%. The results show some limitations in the application of the linear regression model.

Buildings account for a large portion of the total energy use in the US; therefore, improving the operation of typical variable-air-volume (VAV) systems in buildings can provide a tremendous economic opportunity. ASHRAE Guideline 36 recommends a resetting strategy for supply air temperature (SAT) for VAV systems based on outside air temperature. However, this strategy may not produce optimal performance, particularly when simultaneous cooling and heating occurs in zones. In addition, there is no strategy recommended in the Guideline to reset the zone minimum airflow set point in a single-duct VAV terminal unit with reheat, although this setpoint has a great impact on zone reheat requirements and ventilation efficiency. Thus, this paper introduces new strategies to reset both the SAT and zone minimum airflow rate set points to improve the efficiency of typical VAV systems. The strategies were tested under various conditions through experiments performed in fully instrumented VAV systems located in the HVAC lab at the University of Cincinnati. The experiments were conducted on a chilled-water VAV system that serves three controlled zones with hot-water reheat VAV boxes controlled by a typical commercial BACnet web-based building automation system BAS. The simulation studies were performed using the building energy simulation software EnergyPlus to evaluate the strategies at a larger scale in various locations. The simulation results show that the proposed resetting strategies can provide fan energy savings between 1.6% and 5.7% and heating load savings between 7.7% to 33.7%, depending on the location. The laboratory testing shows that the proposed strategies can provide stable control performance in actual systems as well as achieving the anticipated reheat and fan energy savings. The result offers significant improvements that can be implemented in the Guideline for single-duct VAV system operation and control.

A rise in the number of EVs (electric vehicles) in Europe is putting pressure on power grids. At an urban scale, Positive Energy Districts (PEDs) are devised as archetypes of (small) urban districts managing a set of interconnected buildings and district elements (lighting system, vehicles, smart grid, etc.). This paper offers a comprehensive analysis of the impact of e-mobility in a PED, simulated using MATLAB-Simulink software. The PED, a small district in northern Spain, is assessed in five scenarios representing varying requirements in terms of energy efficiency of buildings, type of street lighting and number of EVs. The results suggest that the energy rating of the buildings (ranging from A for the most efficient to E) conditions the annual energy balance. A PED with six interconnected buildings (3 residential and 3 of public use) and 405 EVs (as a baseline) only achieves positivity when the buildings have a high energy rating (certificate A or B). In the most efficient case (A-rated buildings), simulation results show that the PED can support 695 EVs; in other words, it can provide nearly 9 million green kilometres. This result represents a potential 71% saving in carbon emissions from e-mobility alone (as compared to the use of fossil-fuel vehicles), thus contributing a reduction in the carbon footprint of the district and the city as a whole.

• A methodology was represented for calibrating the building energy performance model. • Accurate calibration was achieved on the model even with the short-term measurement. • The calibration process was utilized the MBE- CVRMSE and obtained an enhanced accuracy. • The HDD and CDD was applied in the absence of the real weather data. • The accurate model also provides the opportunity to trail the systems' performance. The calibration process has an excellent advantage, especially for large-scale projects that manage the potentially complex energy performances of various building systems. It also provides great advantages when the annual real-time measurements are not available since the created model can be calibrated with short-term measurements to obtain an accurate annual energy performance. This paper presents a methodology for creating an energy performance model based on a calibration process of a large-scale residential building incorporating heating and cooling energy consumptions. The main purpose is to calibrate the building energy model by utilizing mean bias error (MBE) and coefficient of variation of root-mean-squared error (CVRMSE) indices and obtain the annual building-energy performance with enhanced accuracy. Furthermore, two novel approaches were developed. The first is to provide accurate weather data in the absence of real weather data by taking advantage of available data for heating degree days (HDDs) and cooling degree days (CDDs). The second is to provide accuracy on the collected data of measurements by taking advantage of the predicted data of short term measurements. A methodology is evolved to calibrate the building model by using real-time short-term monitored data from the building. The methodology consists of four main steps and further sub-steps. Modeling, the first step, covers the collection of input data and the development of the model stages. Measurements, the second step, covers the process of data collection for calibration. Improvements for calibration represent the third step, where different versions of the model are developed by changing the input parameters of the model within the defined ranges of variation (ROV). Then, the results of the obtained versions are compared with the real-time measurements, and MBE and CVRMSE values for each version calculated to examine the model's accuracy. Finally, when the model is considered as calibrated, the simulation is run to obtain the annual energy performance of the building for better precision. The results in the model run here show the monthly MBE and CVRMSE values for the initial version to be 15.12% and 17.19%, respectively. After applied strategies, these values are improved according to the calibration criteria defined by ASHRAE to −2.76% and 8.40% respectively, which is considered as calibrated. Overall, it is usually hard and costly to track the real-time energy consumptions of buildings for a long period. Annual building energy consumptions can be obtained accurately by calibrating the building model with the short-term measurements. An accurate model gives the opportunity to trial the performance of each building system. [ABSTRACT FROM AUTHOR]

• A novel-low thermal conductive insulations were used for the building applications. • Energy and cost analysis were made for different climate region and fuels. • Optimum insulation thickness was decided for highest energy and cost saving. • An annual savings of about 14 % can be achieved for the optimum insulation. In this study, energy analysis was carried out to decide the building energy performance for different climate regions and fuels with various novel polyurethane insulation materials. The coal, natural gas, liquefied-petroleum-gas (LPG), fuel-oil and electricity were considered as the fuel. The results show that 3% paper mill sludge (PMS) added polyurethane foam has the best thermal resistance. When 3% PMS added polyurethane is compared with the standard one, the largest heat need reduction occurs in the 1st, 2nd, 3rd and 4th regions to be 14.6 %, 13.57 %, 13.14 % and 12.57 %, respectively, for the insulation thickness of about 0.049 m. 3% PMS added polyurethane can provide the largest energy saving cost of 1.456 $/m2.year for LPG in the fourth region for the insulation thickness of 0.0245 m. It is followed by electricity, fuel oil, coal and natural gas to be 1.033 $/m2, 0.97 $/m2, 0.456 $/m2 and 0.23 $/m2, respectively. In the case of 0.0245 m thickness, life-cycle saving varies between 8.86–54.6 $/m2 and the payback period changes between 1.37 years and 8.76 years depending on the fuels. The new polyurethane reduces the required thickness of the insulation materials about almost halve for all region thereby reducing the insulation material cost. [ABSTRACT FROM AUTHOR]

• New ISO 52016-1:2017 standard compared to the dynamic simulation model in TRNSYS. • Analysis done for a wide range of building conditions/characteristics. • Differences between the two methods up to 40% for heating and up to 18% for cooling. • Relatively large differences due to heat transfer through multi-pane windows. • Certain discrepancy due to the calculation of heat transfer through opaque elements. In 2017, the ISO 52016-1:2017 standard introduced a new methodology for the assessment of the building energy performance as a replacement for the simplified method used in the ISO 13790:2008 standard. The capabilities of the new standard have been considerably improved with respect to those of the old standard, but currently there is a lack of analyses that assess the accuracy and limitations of the new standard applied to realistic multi-zone buildings of various types and in various climates. This paper presents a comprehensive analysis of the model accuracy for a wide range of building uses, envelope properties, climates, and heating/cooling needs, ranging from 5 kWh/m2 to 216 kWh/m2 for heating and from 23 kWh/m2 to 170 kWh/m2 for cooling. The analysis was done by comparing the results from the ISO 52016-1:2017 standard with the dynamic simulation model in TRNSYS. The differences between the two methods in the annual energy needs are up to 40% for heating and up to 18% for cooling. Furthermore, it is shown that the use of constant values of solar energy transmittance and the overall heat transfer coefficient for windows can cause substantial errors in the calculation of building energy needs, more so for buildings with high-performance windows. Although a certain level of discrepancy between the ISO 52016-1:2017 standard and TRNSYS also occurs in the calculation of heat transfer through opaque elements, the differences seem to be less pronounced than for windows. [ABSTRACT FROM AUTHOR]

Abstract Over the last two decades, there has been a growing realization that the actual energy performances of many buildings fail to meet the original intent of building design. Faults in systems and equipment, incorrectly configured control systems and inappropriate operating procedures increase the energy consumption about 20% and therefore compromise the building energy performance. To improve the energy performance of buildings and to prevent occupant discomfort, adverse condition and critical event prediction plays an important role. The Adverse Condition and Critical Event Prediction Toolbox (ACCEPT) is a generic framework to compare and contrast methods that enable prediction of an adverse event, with low false alarm and missed detection rates. In this paper, ACCEPT is used for fault detection and prediction in a real building at the University of Southern Denmark. To make fault detection and prediction possible, machine learning methods such as Kernel Density Estimation (KDE), and Principal Component Analysis (PCA) are used. A new PCA–based method is developed for artificial fault generation. While the proposed method finds applications in different areas, it has been used primarily for analysis purposes in this work. The results are evaluated, discussed and compared with results from Canonical Variate Analysis (CVA) with KDE. The results show that ACCEPT is more powerful than CVA with KDE which is known to be one of the best multivariate data-driven techniques in particular, under dynamically changing operational conditions.

Earth-to-air heat exchangers (EAHEs) can be used in the ventilation systems of various types of buildings. Multipipe structures can be found in large-volume buildings, yet scientific analysis of such systems is rare. Annual energy gains and electricity consumption for equivalent single-pipe and multipipe systems are typically not available. This paper bridges this gap, presenting the results of experimental studies on pressure losses in three-, five- and seven-pipe EAHEs and analysis for the annual energy gains and electric energy consumption as compared to a single-pipe exchanger. The results showed that the multipipe EAHE can be successfully replaced by a single-pipe structure with the same thermal performance and similar pressure losses if a tube with the appropriate diameter is used. However, multipipe heat exchangers can also use pipes of larger diameter (manifolds and/or branches), which improves their energy efficiency and may then make them more advantageous than single-pipe structures. From this reason, ultimately, the final selection of exchanger geometry should take into account economic and environmental issues and also user preferences and their importance in the hierarchy.

The paper presents an analysis of the application of an air-to-water electric compressor heat pump (AWHP) for the recovery of waste heat from the exhaust air in a typical multifamily residential building and the use of this heat for space heating, as well as the impact of this solution on the building energy performance (the PPR index). Simulations were performed in TRNSYS for five locations in Poland (Koszalin, Wrocław, Lublin, Białystok, Suwałki), for various heating system parameters (80/60 °C, 75/65 °C, 70/50 °C, 55/45 °C, 35/28 °C), for various temperature limitations of heat pump operation. It was shown that the analyzed system has great potential from an energy and environmental point of view. It can provide significant benefits in terms of the energy performance of the building, depending on the system parameters. The results show that the most energy-efficient system is the one with the lowest heating system temperatures. Moreover, implementing a temperature limitation on the heat pump operation improves its efficiency, but the higher the design parameters of the heating installation and the lower the limitation, the lower the heat pump contribution, and the higher the SCOP and the PPR. The energy effect is also influenced by location, but its scale depends on the parameters of the heating system and the temperature limitation of the heat pump’s operation. It is more significant for lower heating system parameters. This system enables the possibility of further reducing the demand for nonrenewable primary energy by powering the heat pump with photovoltaic cells.

In this study, life cycle assessment was applied to a novel liquid flow window, based on the cases of sports center application in different climates of Hong Kong and Beijing. The economic, environmental and energy impacts of selected window frame material, the expected lifetime and cost of the working fluid and accessory components were assessed. It was found that the use of wooden frame could be advantageous in that the cost payback time is less than 1.4 years in Hong Kong and well below 5.5 years in Beijing. Nevertheless, when stainless steel frame was in use, the cost payback period would increase to 3.0 and 7.0 years for Hong Kong and Beijing respectively. The lengthy payback time of Beijing is mainly caused by the use of expensive anti-freezer in the liquid circulation. Sensitivity test showed that by taking efforts to lower the material costs and to prolong the lifetime of working liquid, the cost payback time in Hong Kong and Beijing could be reduced to 1.5 and 3.6 years or lower. [ABSTRACT FROM AUTHOR]

Increases in fossil fuel usage and energy consumption have become important issues in most countries of the world. Recent statistics show that the building sector is the main contributor to the world total energy consumption, which makes it responsible for a significant amount of greenhouse gas emissions. Several studies have been carried out to design buildings focusing on reducing their energy consumption and improving occupants' comfort. This study focused on the impact of parameters influencing the seasonal transition periods (STPs) and annual energy consumption through numerical simulations in residential buildings. For this purpose, the thermal performance of six different wall constructions with similar U values under different ventilation rates was investigated. In addition, the effect on building heating and cooling loads during spring and autumn STPs in buildings with diurnal occupation patterns in hot, arid climates was studied. A small building located in Tehran, Iran, was selected as a case study model and simulated as the reference building. The results showed that Wall 4 with an effective surface mass of 249 kg/m2 had the best performance in reducing energy consumption and had a significant effect on STPs. When Wall 2's ventilation rate reached 10 ach (air changes per hour), its impact on STPs was significant within the 60 days of the autumn transition period. The effect of natural ventilation on STPs was minimal for Wall 4. It was also found that a 5 ach ventilation rate was the most appropriate for this period. [ABSTRACT FROM AUTHOR]

Estimation of a building's heating and cooling loads is an important factor taken into account implementation of energy saving measures in order to enhance energy performance of the building. In this work, the heating and cooling loads are predicted to enhance the building energy performance using different types of artificial neural networks namely, Elman network, recurrent network and back propagation network. The effect of eight input variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) on two output variables (heating load and cooling load of residential buildings) is studied. The collected features are given as input to various neural networks for predicting the heating and cooling loads. The performance of the method is calculated in terms of mean absolute error, mean square error and mean relative error. Among all the networks back-propagation neural network has highest accuracy. The mean absolute error in predicting the loads is found to be 0.1 for heating load and 0.1254 for cooling load which is much better than already existing methods. The results of the work further reinforce the fact that ANN is an important tool for prediction and analysis of energy performance of a building. [ABSTRACT FROM AUTHOR]

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Cities that expand and grow with new areas allowed for construction as a result of a rapidly increasing population have become the centre of global energy consumption. Most of the energy consumed is still based on fossil fuels, which is considered the most important cause of the rise in greenhouse gases and thus the deepening climate crisis. Therefore, the transformation of urban areas into energy-efficient and low-carbon cities has become a necessity to create a sustainable future. The climate data-based evaluation of the relationship between buildings, which have the largest energy-saving potential, and urban geometry, which affects the energy performance of buildings in the long term, is crucially important. This study aims to evaluate the potential impacts of climate change on the energy performance of buildings at the settlement scale. For this purpose, changes in building energy performance within different settlement pattern alternatives created for Istanbul (a temperate-humid climate), where new settlement patterns are rapidly developed, were evaluated according to present and future weather files. The evaluation results have demonstrated that sustainable energy use in urban areas is only possible through the integration of a building's energy performance, urban geometry and climate change in the planning and design stages. [ABSTRACT FROM AUTHOR]

As building systems account for almost half of the total energy consumed by the building sector to provide space heating, cooling, and ventilation, efficiently designing these systems can be the key to energy conservation in buildings. Dual VAV systems with an effective control strategy can substantially reduce the energy consumption in buildings, providing a significant scope of further research on this system configuration. This paper proposes to utilize the warm air duct of the dual VAV system as a dedicated outdoor air (DOA) unit when no heating is required, which allows the cooling load to be effectively distributed between two ducts. A specific control sequence is proposed with different supply air temperature reset strategies to estimate the heating, cooling loads, and fan power energy consumption of the proposed system. A simple two-zone office building is taken as a preliminary case study to simulate the airflow rates and fan power of a single duct VAV and proposed dual VAV systems to illustrate the concept. Finally, a larger multi-zone office building is simulated to measure the annual heating, cooling loads, and fan power energy and compare the energy savings among the systems. The results show significant fan power reduction ranging from 1.7 to 9% and notable heating energy reduction up to 76.5% with a small amount of cooling load reduction varying from 0.76 to 2.56% depending on the different locations for the proposed dual VAV systems. Further energy savings from different supply air temperature reset strategies demonstrate the opportunity of employing them according to climates and case studies. The proposed dual VAV system proves to have the potential to be adapted in buildings for the purpose of sustainability and energy savings.

Buildings and the mobility sectors are the two sectors that currently utilize large amount of fossil-based energy. The aim of the paper is to, critically analyse the integration of electric vehicles (EV) energy load with the building’s energy load. The qualitative and quantitative methods are used to analyse the nearly/net zero energy buildings and the mobility plans of the Europe along with the challenges of the plans. It is proposed to either include or exclude the EV load within the building’s energy load and follow the emissions calculation path, rather than energy calculation path for buildings to identify the benefits. Two real case studies in a central European climate are used to analysis the energy performance of the building with and without EV load integration and the emissions produced due to their interaction. It is shown that by replacing fossil-fuel cars with EVs within the building boundary, overall emissions can be reduced by 11–35% depending on the case study. However, the energy demand increased by 27–95% when the EV load was added with the building load. Hence, the goal to reach the nearly/net zero energy building target becomes more challenging. Therefore, the emission path can present the benefits of EV and building load integration.

This paper presents basic principles of built-environment physics’ modelling, and it reviews common computational tools and capabilities in a scope of practical design approaches for retrofitting purposes. Well-established simulation models and methods, with applications found mainly in the international scientific literature, are described by means of strengths and weaknesses as regards related tools’ availability, easiness to use, and reliability towards the determination of the optimal blends of retrofit measures for building energy upgrading and Urban Heat Island (UHI) mitigation. The various characteristics of computational approaches are listed and collated by means of comparison among the principal modelling methods as well as among the respective computational tools that may be used for simulation and decision-making purposes. Insights of coupling between building energy and urban microclimate models are also presented. The main goal was to provide a comprehensive overview of available simulation methods that can be used at the early design stages for planning retrofitting strategies and guiding engineers and technical professionals through the simulation tools’ options oriented to the considered case study.

A building energy performance gap can be illustrated as the difference between the theoretical (methodologically defined) and the actual energy consumption. In EU countries, Energy Performance Certificates are issued when buildings are constructed, sold, or leased. This information is the first step in order to evaluate the energy performance of the building stock. In Serbia, when issuing an energy certificate, the adopted national methodology recognizes only energy consumption for heating. The main purpose of this paper is to evaluate the energy gap and estimate the relevance of an Energy Performance Certificate to meet the national energy efficiency or carbon target. An Energy Performance Certificate determines the theoretical residential and commercial building energy efficiency or its “design intent”. This research stresses the necessity of measuring and achieving reductions in actual energy consumption through system regulation and consumers’ self-awareness in buildings. The research compares the performance of the building stock (135) that is connected to the District Heating System (DHS), with its own integrated heat meter, to Individual Gas Boiler (IGB) systems (18), in the city of Novi Sad, Serbia, built after 2014. For the purpose of comparing energy consumption, 16 buildings were selected that are very similar in terms of design, operation, and location. The data used are derived from metered consumption data, official evidence of city service companies, and Energy Performance Certificates of the considered buildings. We have determined that IGB systems have a much wider specific annual performance gap (11.19–101 kWh/m2a) than the buildings in the DHS (3.16–18.58 kWh/m2a).

Tall buildings are being designed and built across a wide range of cities. A poorly designed tall building can tremendously increase the building’s appetite for energy. Therefore, this paper aims to determine the design strategies that help a high-rise office building to be more energy efficient. For this purpose, a comparative study on twelve case buildings in three climate groups (temperate, sub-tropical & tropical) was performed. The exterior envelope, building form and orientation, service core placement, plan layout, and special design elements like atria and sky gardens were the subject of investigation. The effectiveness of different design strategies for reducing the cooling, heating, ventilation and electric lighting energy were analysed. Finally, lessons from these buildings were defined for the three climates. Furthermore, a comparison of building energy performance data with international benchmarks confirmed that in temperate and sub-tropical climates sustainable design strategies for high-rise buildings were performing well, as a result leading to lower energy consumption. However, for the tropics the design of high-rise buildings needs higher concern.

The building energy performance pattern is predicted to be shifted in the future due to climate change. To analyze this phenomenon, there is an urgent need for reliable and robust future weather datasets. Several ways for estimating future climate projection and creating weather files exist. This paper attempts to comparatively analyze three tools for generating future weather datasets based on statistical downscaling (WeatherShift, Meteonorm, and CCWorldWeatherGen) with one based on dynamical downscaling (a future-typical meteorological year, created using a high-quality reginal climate model). Four weather datasets for the city of Rome are generated and applied to the energy simulation of a mono family house and an apartment block as representative building types of Italian residential building stock. The results show that morphed weather files have a relatively similar operation in predicting the future comfort and energy performance of the buildings. In addition, discrepancy between them and the dynamical downscaled weather file is revealed. The analysis shows that this comes not only from using different approaches for creating future weather datasets but also by the building type. Therefore, for finding climate resilient solutions for buildings, care should be taken in using different methods for developing future weather datasets, and regional and localized analysis becomes vital.

In recent years, investigations on advanced technological solutions aiming to achieve high-energy performance in buildings have been carried out by research centers and universities, in accordance with the reduction in buildings’ energy consumption required by European Union. However, even if the research and design of new technological solutions makes it possible to achieve the regulatory objectives, a building’s performance during operation deviates from simulations. To deepen this topic, interesting studies have focused on testing these solutions on full-scale facilities used for real-life activities. In this context, a test facility will be built in the university campus of Politecnico di Torino (Italy). The facility has been designed to be an all-electric nearly Zero Energy Building (nZEB), where heating and cooling demand will be fulfilled by an air-source heat pump and photovoltaic generators will meet the energy demand. In this paper, the facility energy performance is evaluated through a dynamic simulation model. To improve energy self-sufficiency, the integration of lithium-ion batteries in a HVAC system is investigated and their storage size is optimized. Moreover, the facility has been divided into three units equipped with independent electric systems with the aim of estimating the benefits of local energy sharing. The simulation results clarify that the facility meets the expected energy performance, and that it is consistent with a typical European nZEB. The results also demonstrate that the local use of photovoltaic energy can be enhanced thanks to batteries and local energy sharing, achieving a greater independence from the external electrical grid. Furthermore, the analysis of the impact of the local energy sharing makes the case study of particular interest, as it represents a simplified approach to the energy community concept. Thus, the results clarify the academic potential for this facility, in terms of both research and didactic purposes.

This paper reports the application of RC dynamic models for assessing thermal performance of buildings from in-situ tests (obtaining the U value for the walls, and the UA value and gA value for the whole buildings). The following aspects which are relevant to this approach have been systematically analyzed: The effect of the solar radiation on the heat flux through the opaque walls versus the performance of the models including this effect, the optimum number of nodes required to represent the thermal systems, the assignment of inputs and outputs and the length of the test period. Additionally, several options modelling relevant effects using unmeasured variables were studied to evaluate the feasibility to reduce the cost and intrusiveness of the measurement devices required to obtain accurate results. Data series recorded under different experimental conditions were considered to analyze the robustness and validity of the results. The performance of the models for each of these different test conditions is discussed. The uncertainties estimated using the described method for the U values of the opaque walls, and the UA and gA values of the whole building, are, respectively, 2.8%, 4.2% and 2.3%. The feasibility to model relevant effects using unmeasured variables has been demonstrated. A simplified and well-known building has been used as a case study, reinforcing and complementing the validation criteria.

The evidences of the influence of climate change (CC) in most of the key sectors of human activities are frequently reported by the news and media with increasing concern. The building sector, and particularly energy use in the residential sector, represents a crucial field of investigation as demonstrated by specific scientific literature. The paper reports a study on building energy consumption and the related effect on indoor thermal comfort considering the impacts of the Intergovernmental Panel on Climate Change (IPCC) 2018 report about temperature increase projection. The research includes a case study in New York City, assuming three different scenarios. The outcomes evidence a decrease in energy demand for heating and an increase in energy demand for cooling, with a relevant shift due to the summer period temperature variations. The challenge of the last decades for sustainable design was to increase insulation for improving thermal behavior, highly reducing the energy demand during winter time, however, the projections over the next decades suggest that the summer regime will represent a future and major challenge in order to reduce overheating and ensure comfortable (or at least acceptable) living conditions inside buildings. The growing request of energy for cooling is generating increasing pressure on the supply system with peaks in the case of extreme events that lead to the grid collapse and to massive blackouts in several cities. This is usually tackled by strengthening the energy infrastructure, however, the users’ behavior and lifestyle will strongly influence the system capacity in stress conditions. This study focuses on the understanding of these phenomena and particularly on the relevance of the users’ perception of indoor comfort, assuming the IPCC projections as the basis for a future scenario.

Multi-use venue buildings are an extreme example of buildings with flexible use. Such buildings have high occupant diversity factors and inconsistent occupant-building interaction leading to highly changeable demands of the building services. Intensive monitoring of a case study multi-use venue building was carried out in order to analyse its energy performance and identify energy saving opportunities. Results are presented from three distinct uses of the building, with each analysed as a separate case study. The analysis focused on exploring relationships between variables for each event; identifying multiple energy savings opportunities. Poor building management system (BMS) scheduling led to 49% of total gas use across all three events being estimated as waste, whereas inappropriate set points accounted for 7%. Event organiser behaviour led to 6% of total electricity across all three events estimated as waste, whereas inflexible controls accounted for 2%. Based on these results, venue building managers may find that prior knowledge of building activity and more accurate occupancy hours could yield significant energy savings through proactive energy management. [ABSTRACT FROM AUTHOR]

This communication presents ModSCO, a web application that supports systematic energy performance evaluation using Reduced Order Models (ROM). These models are particularly useful in scenario with missing, incomplete or uncertain building information. The paper describes the theory behind ROM grey-box modelling and presents case studies that support the smart operation of energy systems by generating Energy Conservation Opportunities (ESCOs) for instance, to help ISO 50001 implementation. The ROM demonstrated to provide accurate results with a reduced effort. The acceptable calibration tolerance provided by the ASHRAE Guideline 14 is been used to demonstrate the ROM’s accuracy. Additionally, the ModSCO architecture and user interface is also described.