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Reducing energy consumption in buildings and increasing renewable production are key goals of European policies to achieve a sustainable and competitive low-carbon economy by 2020 and beyond. Non-residential buildings constitute a heterogeneity sector characterized by high energy consumption and various building types, sizes and energy characteristics over Europe. This paper presents the overall results of the data collected by the GreenBuilding Programme (GBP), launched in 2006 to promote and improve energy efficiency in new and existing European non-residential buildings. The GPB involved building owners willing to adopt energy efficiency measures to decrease energy consumption of their buildings by of at least 25%. Based on voluntary participation, hundreds of partners joined the project, which collected data from more than a thousand buildings of different age, size, use and type (such as offices, hotels, and industry). This paper provides an overview of the Programme and its main results up to its completion in 2014. The paper focuses on building characteristics, energy performance, efficiency measures and energy savings, which are globally estimated to be around 985 GWh/year. A more detailed focus is then given to office buildings, which represent the most frequent building category in the Programme. Case studies are presented to show best practices in various countries with consolidated energy efficiency policy strategies. The paper categorises the main technological measures related to envelope, appliances and systems. It shows how a wide range of technologies are becoming an integral part of buildings and how technology plays a major role in exploiting the massive potential benefits of reducing building energy consumptions. The analysis of the results generates a reliable snapshot of European non-residential building stock. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the main pillars of the European energy strategy and it involves all the economic sectors. The present study proposes an analysis of the energy saving opportunities available in the Italian hotel sector, which has a relevant importance in the national context. The paper introduces a comprehensive model of the Italian hotel sector developed in the LEAP (Long-range Energy Alternatives Planning) environment. The first aim is to estimate the baseline of the consumption, then to evaluate the maximum potential energy saving and, finally, to assess the implementation of a realistic energy efficiency scenario. The paper demonstrates that it may be possible to achieve primary energy savings of 1.6 TWh (13%) in 2030 by implementing financially sustainable energy efficiency measures. [ABSTRACT FROM AUTHOR]

Abstract In recent years, technological advances have substantially extended the capabilities of building automation. Despite the achieved advances, evidently, automation have not been widely adopted by occupants in buildings. To enhance automation adoptability, automation procedure in buildings should involve determination of user's preferred automation levels, in different conditions and control contexts and learning preference dynamics in time. In line with this motivation, in this paper we introduce a building automation that learns occupant's preferences continuously to fully or partially control the service systems in buildings based on a set of dynamic rules that are generated with the insight about user's preferences and activities. The algorithmic components of our proposed automation include (1) dynamic command planning, (2) adaptive local learning, and (3) iterative global learning. In order to evaluate these algorithms, we used a combination of real and synthetic user activity and preference data from an office with five occupants and an apartment with one occupant. Based on our results from evaluation of adaptive local learning, after a certain number of days (i.e., 8.5 days in average) the accuracy of predicting participants' preference reached to an acceptable value (i.e., above 85%). About 24% to 75%, 5% to 45%, and 6% to 49% of the total daily energy consumption of the participants could be saved using full automation, adaptive automation and inquisitive automation, respectively. Our results for evaluating iterative global learning algorithm showed that adaptive automation has the highest sum of the rewards from achieved benefit and user satisfaction and inquisitive automation has the second highest reward values. Full automation and no automation came in third and last spots, respectively. [ABSTRACT FROM AUTHOR]

Highlights • A novel cloud computing application for measurement and verification 2.0 is presented. • IntelliMav offers a solution for near real-time energy savings verification. • Powerful machine learning regression techniques are automatically applied to energy data. • Parallel processing using powerful cloud computing infrastructure reduces resources required. • A real-world case study demonstrates the ease of use and benefits of the application. Abstract Energy conservation measures (ECMs) are implemented in all sectors with the objective of improving the efficiency with which energy is consumed. Measurement and verification (M&V) is required to verify the performance of every ECM to ensure its successful implementation and operation. The methodologies implemented to achieve this are currently evolving to a more dynamic state, known as measurement and verification 2.0, through the use of automated and advanced analytics. The primary barrier to the adoption of M&V 2.0 practices are the tools available to practitioners. This paper aims to populate the knowledge gap in the industrial buildings sector by presenting a novel cloud computing-based application, IntelliMaV, that applies advanced machine learning techniques on large datasets to automatically verify the performance of ECMs in near real-time. Additionally, a performance deviation detection system is incorporated, ensuring persistence of savings beyond the typical period of analysis in M&V. IntelliMaV allows M&V practitioners to quantify energy savings with minimum levels of uncertainty by applying powerful analytics to data readily available in industrial facilities. The use of a cloud computing-based architecture reduces the resources required on-site and decreases the time required to train the baseline energy model through the use of parallel processing. The robust nature of the application ensures it is applicable across the broad spectrum of ECMs in the industrial buildings sector. A case study carried out in a large biomedical manufacturing facility demonstrates the ease of use of the application and the benefits realised through its adoption. The energy savings from an ECM were calculated to be 2,353,225 kWh/yr with 25.5% uncertainty at a 90% confidence interval. [ABSTRACT FROM AUTHOR]

Highlights • This study presents a detailed review on IRT for the investigation of building envelope defects. • The literature survey is conducted regarding existing IRT methodologies for building envelopes. • The previous studies with measurement methods, analysis schemes and analysis types are categorized in the literature matrix. • Recent advances and future trends on IRT methods are presented. Abstract Thermal radiation generated by limited resources is emitted from the surfaces and interpreted as temperature. On the other hand, the limited resources necessitate prevention of energy leakages causing noticeable temperature differences on surfaces. Providing sensitive information on temperature contrast, infrared thermography (IRT) is a prominent tool for detecting infrared energy on the surfaces of the objects. IRT is also very promising method for detecting thermal irregularities, air leakages and even moisture abnormalities on building envelopes. Since the concept of nearly zero energy buildings is to be the main target of energy policies in European Union published by European Commission, improving thermal performance of buildings becomes crucial in the last decade. Therefore, implementation of IRT for visualizing and analyzing concealed defects on building envelopes is very promising for improving the energy efficiency of buildings. Given recent regulations and benefits of emerging thermogram technologies mentioned above, a detailed review of the previous studies investigating the abnormalities of building envelopes by IRT is presented in this paper. Furthermore, the applications with measurement methods, analysis schemes and analysis types are classified to highlight the potential of IRT in inspection of building envelopes for providing energy efficient solutions. Finally, recent advances and practical future opportunities are presented for additional contribution of the IRT methods previously employed. [ABSTRACT FROM AUTHOR]

Highlights • A highly interactive web tool for the exploration of NILM results is presented. • Data cube allows insightful integration of NILM components with raw consumption data. • Hospital total demand, 7 cooling machines and temperature each minute for a year. • Fluid feedback by live coordinated views allows to interpret the NILM decomposition. • NMF exploits positivity of energy consumptions for an interpretable decomposition. Abstract Non-intrusive load monitoring (NILM) techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the expert user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, non-negative and sum up the original measured quantity. Such components reveal hidden temporal patterns which may be difficult to interpret in complex systems such as large buildings. We suggest to integrate the knowledge of the user into the analysis in order to recognize the real events inside the electric network behind the learnt patterns. In this paper, we integrate the available domain knowledge of the user by means of a visual analytics web application in which an expert user can interact in a fluid way with the NMF outcome through visual approaches such as barcharts, heatmaps or calendars. Our approach is tested with real electric power demand data from a hospital complex, showing how the interpretation of the decomposition is improved by means of interactive data cube visualizations, in which the user can insightfully relate the NMF components to characteristic demand patterns of the hospital such as those derived from human activity, as well as to inefficient behaviors of the largest systems in the hospital. [ABSTRACT FROM AUTHOR]

This study investigates an energy-inefficient council housing tower block in London, due for retrofit, by exploring the correlations between occupancy and energy consumption patterns, thermal comfort and building energy performance in the winter. The aim of this study is to evaluate the building performance and develop a reliable building simulation model to be employed in the second phase of the study for developing an energy-efficient and cost-effective retrofit strategy to support the council's plans. The research seeks to demonstrate the significance of using dominant occupancy and energy use profiles as opposed to relying on standardised profiles when calculating building energy consumption using building simulation software. The paper reports on the results from the first phase of the study where a quantitative methodology; including a questionnaire, structured interviews, indoor monitoring, and building simulation modelling was adopted. The results provide evidence that occupants’ socio-demographic characteristics have a considerable impact on household energy consumption and fuel bills. The results also show that indoor environmental issues experienced in many flats are partly attributed to the thermally inefficient building envelope and partly due to occupants’ patterns of operating their homes with evidence of excessive heating energy use in attempts to alleviate the indoor issues experienced. [ABSTRACT FROM AUTHOR]

This paper presents the application of multi-objective genetic algorithms for holistic building design that considers multiple criteria; building energy use, capital cost, daylight distribution and thermal indoor environment. The optimization focus is related to building envelope parameters. To obtain relevant feedback from multi-objective optimizations in early design stages, evaluation speed is a key concern. The paper presents a fast evaluation method fit for the early design stages. It uses a combination of two different quasi-steady-state methods for energy and indoor environment evaluations, a Radiance implementation for daylight simulations and a scripted algorithm for capital cost evaluations. The application of the method is developed around an integrated dynamic model which allows visual design feedback from all evaluations to be an integrated part of the design tool experience. It is concluded, that quasi-steady-state methods implemented as part of integrated dynamic models are fast and flexible enough to support building energy-, indoor environment- and cost-optimization the early design stages. [ABSTRACT FROM AUTHOR]

The selection of a thermal comfort model for establishing indoor optimal hygrothermal conditions during the hot period has a major impact on energy consumption of Net Zero Energy Buildings in hot climates. The objective of this paper is to compare the influence of using different thermal comfort models for zero energy buildings in hot climates. The paper compares the impact of applying Fanger's model, Givoni's model, the ASHRAE 55 adaptive comfort model and the EN 15251 adaptive comfort model on energy consumption and comfort performance. Using both the building performance simulation tools ZEBO and EnergyPlus for energy simulation, an existing prototype of a residential apartment module is used to evaluate energy performance and thermal comfort in two parametric series. The first one is the result of coupling natural ventilation and mechanical cooling and the second one is guided coupling natural ventilation, mechanical cooling and ceiling fans. This study shows that the percentage of energy consumption difference meeting the comfort criteria according to ISO 7730 in comparison to EN 15251, ASHRAE 55 or Givoni's model varied up to 16%, 21% and 24.7%, respectively for the presented case study. More energy savings can be expected for buildings in hot climates with greater cooling demands. [ABSTRACT FROM AUTHOR]

The gas turbine capacity and operating strategy selection of Building Cooling, Heating and Power (BCHP) systems play an important role in the system operating performance, leading to different primary energy consumption for the same building load. However, the optimal design problems of those two factors have not been solved very well. This paper presents a new simple method to determine the optimal gas turbine capacity and operating strategy. The gas turbine electricity efficiency, the Coefficient of Performance ( COP ) of absorption heat pumps, and the demand heat/power ratio are found to be the key factors. There exists two values of the critical gas turbine efficiency. More primary energy will be consumed when the selected gas turbine electricity efficiency is below the smaller critical value compared with the separated generation. When the selected gas turbine electricity efficiency is between the two critical values, the hourly optimal operating strategy and the gas turbine capacity can be determined by Following the Thermal Load (FTL). Otherwise, the optimal gas turbine capacity should be sized by Following the Electricity Load (FEL) with the consideration of the actual given conditions. Based on this method, some typical cases with or without TES are analyzed. The results agree well with the conclusions drawn in the current published papers. This method gives a new aspect to understand BCHP analytical design, and can be applied in the engineering area. [ABSTRACT FROM AUTHOR]

This paper details the process and results from the first step of a three-step research process. This first step looks to identify the most predictive pre-retrofit metric of energy consumption to utilize in a model to predict the energy savings post retrofit. The ultimate goal of this research is to predict candidacy for retrofit using only a combination of demographic and home-characteristics data that is available for the entirety of the U.S. residential housing stock. This is important, as utility data is almost always protected for privacy and thus unavailable to assist in targeting where energy efficiency retrofits will be successful. It is found that the best metric is the simplest, total energy consumption divided by total floor area. In addition to evaluating which pre-use metric is most indicative of post retrofit savings, the paper evaluates the endogenous component of pre-use to post use and a potential method to alleviate this endogeneity. The research finds that by removing the year that is used to calculate the savings as the baseline pre-use year removes a portion of the endogeneity. It is also found that one year before the savings base year is the best year to utilize as the base. [ABSTRACT FROM AUTHOR]

The foundations of all methodologies for the measurement and verification (M&V) of energy savings are based on the same five key principles: accuracy, completeness, conservatism, consistency and transparency. The most widely accepted methodologies tend to generalise M&V so as to ensure applicability across the spectrum of energy conservation measures (ECM’s). These do not provide a rigid calculation procedure to follow. This paper aims to bridge the gap between high-level methodologies and the practical application of modelling algorithms, with a particular focus on the industrial buildings sector. This is achieved with the development of a novel, machine learning supported methodology for M&V 2.0 which enables accurate and reliable quantification of savings. A novel and computationally efficient feature selection algorithm and powerful machine learning regression algorithms are employed to maximise the effectiveness of available data. The baseline period energy consumption is modelled using artificial neural networks, support vector machines, k-nearest neighbours and multiple ordinary least squares regression. Improved knowledge discovery and an expanded boundary of analysis allow more complex energy systems be analysed, thus increasing the applicability of M&V. A case study in a large biomedical manufacturing facility is used to demonstrate the methodology’s ability to accurately quantify the savings under real-world conditions. The ECM was found to result in 604,527 kWh of energy savings with 57% uncertainty at a confidence interval of 68%. 20 baseline energy models are developed using an exhaustive approach with the optimal model being used to quantify savings. The range of savings estimated with each model are presented and the acceptability of uncertainty is reviewed. The case study demonstrates the ability of the methodology to perform M&V to an acceptable standard in challenging circumstances. [ABSTRACT FROM AUTHOR]

The paper investigates whether air-water heat exchanger with longitudinal fins along the air flow direction are an appropriate means to reduce pressure loss in mechanical ventilation systems, compared to conventional heating coils. To answer this question, theoretical calculations regarding pressure loss and heat transfer are performed and the results are checked by measurements. It is found that a longitudinal fin heat exchanger with 30 radially arranged fins and an overall length of 830 mm transfers the same heat flow as a conventional heating coil (∅ 125). The pressure loss only adds up to about 60% to 70%, so a reduction of 30% to 40% can be achieved. Additionally it is expected, that the longitudinal fin heat exchanger is more suitable for efficient low temperature heating systems, because of the counter-current heat transfer. The disadvantage of its increased length can be compensated by integrating the longitudinal fin heat exchanger in a duct silencer, which is required anyway. Hence, the energy efficiency of mechanical ventilation system can be increased. [ABSTRACT FROM AUTHOR]

Social housing sector is very important in Brazil, due to the necessity of expansion and investments being placed through a substantial government program. Residential buildings are expected to last at least 50 years according to Brazilian standards. Many residential projects in the sector already perform medium or poorly in terms of energy efficiency and thermal comfort today, and their designs are not analysed considering climate change. Therefore, the aim of this paper is to investigate the result of analysing the thermal and energy performance of social housing projects considering climate change, and to assess the impact on the operational phase of introducing energy efficiency measures in the sector, and exploring methods of adaptation to climate change. A representative project of the lower income sector housing was used as case study with the evaluation of measures through thermal and energy simulation with current and future weather files for the cities of São Paulo and Salvador. Results were compared using predicted energy consumption and cooling and heating degree-hours as indicators. The results highlighted some differences related to the climate scenarios and indicator analysed, and showed that the incorporation of energy efficiency measures in current social housing projects is of fundamental importance to minimize the effects of climate change in the coming decades. [ABSTRACT FROM AUTHOR]

The development of tools for urban environment analysis and representation, as a support for energy retrofitting and urban renewal designing strategies, is a prominent issue for the transition to more sustainable and resilient settlement patterns. This paper aims to develop and test a GIS-based tool for the calculation of solar irradiation, as a factor of the building energy performance, by applying, at the urban scale, the simplified method of the international standard (ISO 13790:2008) proposed by Italian specifications (UNI TS 11300: 2008–2014). This standard is a common cultural reference for technicians, practitioners, decision makers and ordinary citizens because it is the basis for the calculation of Energy Performance Certificates (EPC) for buildings. The standard physical model is simplified, but requires some base data that practitioners have to define from their expertise by an in-situ survey. So the adaptation of the calculation of solar irradiation according to the standard at the urban scale needs some simplifications and typological approaches related to the availability of base data. A simple and repeatable standard consistent methodology for the calculation of the solar irradiation on building surfaces has been developed, adopting commonly available base data on urban morphology such as topographic maps or Digital Terrain Models. A summary on the state of the art of GIS tools for urban solar analysis and the standard algorithms for the calculation of the solar irradiation is also provided. For the study of the practitioner's approach in the use of the standard calculation a questionnaire is set up. The work then illustrates the methodology approach adopted for the tool developing and testing. The discussion focuses on the compliance of the results with standard calculation and on solar irradiation values reliability. Such consistency has been assessed via a sensitivity analysis based on different ideal cases. In order to assess the reliability of the results of the GIS tool, a comparison with the values obtained from a dynamic solar simulation environment (Daysim) is performed. [ABSTRACT FROM AUTHOR]

This paper uses an example of an architect designed and occupied suburban house in the temperate climate of Perth, Western Australia to demonstrate how climatic design and low-technology active systems can deliver thermal comfort in average climatic conditions. However when thermal conditions are more extreme acceptable temperature ranges may not be met. Thermal monitoring in the house over eight days of extreme temperatures in summer and winter shows that acceptable temperature ranges may not be met in winter. During extreme winter conditions south facing rooms fall below comfort conditions by up to 3 K in the late night and early morning. The conclusion drawn is that in naturally ventilated buildings personal and psychological behavioral adaptation must go hand-in-hand with climatic design. This behavioral adaptation may become more important in the future if current climatic extremes become the new normal. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k -Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. [ABSTRACT FROM AUTHOR]

Buildings accounted for half of global electricity consumption in recent years. Accurate occupancy information could improve the energy efficiency and reduce the energy consumption in built environment. Although prior studies have explored various sensing techniques for occupancy sensing, these solutions still suffer from serious drawbacks, e.g. their estimated occupancy information is coarse, extra infrastructure is required, and the privacy of occupants is exposed. In this paper, we present the design and implementation of a novel and practical occupancy sensing system, WinOSS, which is able to provide fine-grained occupancy information thoroughly by leveraging existing commodity WiFi infrastructure along with the WiFi-enabled mobile devices carried by occupants. We have implemented WinOSS in a 1500 m 2 built environment for four weeks to validate its performance. Extensive experimental results demonstrate that WinOSS outperforms existing occupancy sensing techniques, and provides comprehensive fine-grained occupancy information (including occupancy detection, counting and tracking) in an accurate, reliable, cost-effective and non-intrusive manner. [ABSTRACT FROM AUTHOR]

Ubiquitous modernization of building energy and management systems brings tendencies for an effective integration of renewables in buildings. This article shows how solar energy can be in charge of energy savings within a building by integration of solar systems with Trombe wall. Numerical calculations of Trombe wall thermal resistance and its performances in comparison with a common wall are considered. Additionally, energy-efficiency gaining by a combination of PV modules and Trombe wall, using simulations by SAM (System Advisor Model) software is discussed. PV module parameters and their impact on its electricity production are observed and analyzed. The PV module parameters, analyzed in the paper are: the tilt angle α of PV modules, incident angle β of direct sunrays on PV modules, normal beam irradiation N and PV cells operating temperature. With bigger incident angle β of direct sunlight on PV modules, the normal beam irradiation is smaller, which brings less electricity production. The impact of PV module operating temperature can be neglected in comparison with the impact of tilt angle on PV module electricity production. The optimal tilt angle of PV modules for generating maximum annual electricity is around latitude angle. It varies with normal beam irradiation to achieve the maximum monthly electricity production. [ABSTRACT FROM AUTHOR]

Polygeneration is an integrated process that produces two or more energy products from one or more primary energy sources (PES). Polygeneration systems have wide applications in utilities, industrial sectors and building sectors. However, there are still very limited applications of the powerful and practical optimization techniques in polygeneration systems of building sectors comparing to their wide applications in industrial sectors. System optimization is one of effective and reasonable tools to exploit the potential of polygeneration systems. Polygeneration offers potential to fulfill the ambitious target of zero energy building (ZEB) because the interdependence of different energy products can be utilized to provide advantage and flexibility to accommodate more renewable energy sources (RES) in the system. Polygeneration based ZEB can be treated as a type of Polygeneration Microgrid (PM) because PM represents a promising solution to supply electricity, heat and cooling efficiently using local energy sources, including RES. The development of PM is closely associated with building applications. Hence, the objective of the present paper is to give a comprehensive review for optimization techniques of polygeneration systems in buildings. The review was conducted based on integrated design, synthesis and operation optimization (IDSOO) for different scales of systems, ranging from the micro-scale for a single house to the large-scale for a residential complex, including PM to identify difference between traditional systems and PM in terms of optimization. The literature was classified according to solution techniques of IDSOO and characteristics of IDSOO in terms of decision variables, objective functions and constraints. The results showed that there is no significant difference between traditional systems and PM in terms of solution techniques while there is subtle difference between traditional systems and PM in terms of objective function and constraints. Optimization of PM has higher requirements for real-time management to integrate more RES. Optimization of micro- and small-scale systems are more complicated than large-scale ones because there are many constraints from technical, legal and policy viewpoints. [ABSTRACT FROM AUTHOR]

In the UK, 20% of houses were built before 1919 and are protected from energy efficiency requirements that would unacceptably alter their character. To meet carbon emission reduction targets, however, it is necessary to keep the number of buildings exempt from energy efficiency improvements to a minimum. The need to preserve the aesthetic and structural qualities of historic buildings makes energy retrofit complicated and costly but these arguments should not be used to resist change. The research presented in this paper investigates how conservation professionals in the UK approach and sanction energy retrofit measures in historic buildings. It provides an overview of the current UK legislation and guidance relating to energy efficiency in heritage buildings and presents findings from a study focused on the approach of conservation professionals to retrofit slim profile double glazing (SPDG). It finds that there is regional variation to energy retrofit in historic buildings between Scotland and the rest of the UK, and that individual conservation professionals hold different views on the use of SPDG, which leads to inconsistencies in its application. Recommendations are made for a more consistent approach to window upgrade as a means of improving the energy efficiency and comfort of historic buildings and for greater interdisciplinary cooperation to align conservation of energy with conservation of heritage. [ABSTRACT FROM AUTHOR]

As of 2007 more than half of the world’s population is living in urban areas (a figure expected to rise to 60% by 2030). Thus, the liveability of the high-density city is gradually becoming a central point of focus and concern. A successful skyscraper model of urban planning could provide the possibility to increase city-space vertically as opposed to the current continuous expansion outward, which has obvious environmental consequences. However, skyscraper development, as well as all other new construction and gradually the older building stock, has to comply with current strict regulations on building energy efficiency. Contemporary high-rise examples do not present a sustainable solution to an increasing population or as models of prosperity, as they are linked to high-energy demand, environmental and social imbalances. This paper looks at design strategies towards promoting skyscraper energy efficiency by considering a climatically responsive design, where orientation, the thermal properties of the building envelope and the effect of altitude, become the main design tools. Initial simulations were performed for a residential and an office reference structure 100 m high. Different scenarios were implemented for gradually upgrading the building envelope and studying its relationship with the changing microclimate with altitude (wind speed increase and dry bulb temperature drop) between ground and top level. The advanced envelope was then simulated to up to 400 m high (120 stories high), and heating and cooling loads were compared in relation to different building heights and uses. EnergyPlus is used as the main simulation tool as it accounts for wind speed increase and dry bulb temperature drop with height. The location chosen is Tel Aviv, Israel, a city already growing upwards and expected to have a significant increase in skyscraper construction in the coming years. The results of the simulations performed present the base upon which further design strategies can be implemented towards reducing the environmental impact of this challenging building type. [ABSTRACT FROM AUTHOR]

Over the last years there have been frequent changes to buildings’ thermal legislation, to cope with increasing environmental restrictions and a wider consumer awareness. This work has a twofold objective: 1 – provide an overall methodology to evaluate changes in buildings’ thermal regulations; 2 – to assess the economic merit of these regulatory changes, which have a profound economic impact especially on homeowners, tenants and the global urban real estate market, but also on the society through wider environmental impacts. The literature has provided several contributions at the scale of buildings, but few at the scale of cities (or even regions). This paper uses the city of Lisbon (Portugal) as a case study to test the methodology and determine the economic impact of the changes made in the thermal legislation by the Government. The economic assessment was made at 3 levels: financial analysis, economic analysis and social-economic analysis. It was found that the new regulation brings not only environmental benefits but also economic benefits in long term at both individual and social level. It was furthermore concluded that the impact of those benefits are geographically uneven, and old areas of the city with worse energy ratings have better economic indicators. [ABSTRACT FROM AUTHOR]

In recent years, technological advances have substantially extended the capabilities of automation systems in buildings. Despite the achieved advances, automation systems have not been widely adopted by building occupants. This paper presents our investigations on automation preferences of occupants for the control of lighting systems and appliances in residential buildings. A survey was carried out to determine how preferences for level of automation vary by contexts as well as individuals’ personalities and demographic characteristics. The contexts investigated in this study include rescheduling an energy consuming activity, activity-based appliance state control, and lighting control. The collected data from 250 respondents were analyzed using Generalized Linear Mixed Models. Our findings demonstrate that in all context no automation is the least preferred option. For rescheduling an energy consuming activity, an automation level with higher user participation is more preferred. For activity-based appliance state control and lighting control, levels of automation with lower user participation are more preferred. Our findings also indicate that income and education levels and also personality traits of agreeableness, neuroticism and openness to experience affect the preference of particular automation levels over the others. Findings from this study can be used in designing user-centered automation systems that lead to potentially more satisfying operation and hence, could enhance automation acceptability. [ABSTRACT FROM AUTHOR]

One of the major challenge facing the achievement of nZE standards in existing buildings is the economic issue: the evidence of monetary gains of energy savings facing high investment costs seems still rather limited to the investors’ eyes. In this context, LCC methods have gained much importance in recent years. However, they present a limitation due to the notable simplifications and hypothesis usually made for input parameters that may affect the results. In order to overcome this limit, this work suggests a probabilistic LCC based on uncertainty and sensitivity analysis via Monte Carlo methods and illustrates it through a building case study under several retrofit scenarios sighting the target nZE. The methodology allows investigating the economic effectiveness of alternative measures, giving insight into possible ranges of the economic indicator related to a specific design option. The analysis is focused on a micro-economic dimension and based on the availability and reliability of inputs data and on their proper characterization with Probability Density Functions. Variance-based methods for sensitivity analysis are employed to establish the most influential parameters on output uncertainty. The paper demonstrates the potentials of a probabilistic LCC in providing a more realistic decision support about investments for energy efficient projects. [ABSTRACT FROM AUTHOR]

Lighting systems in buildings have substantial influence on both the occupants' productivity and energy efficiency. This paper addresses the problem of minimizing the energy necessary to provide predefined scheduled lighting levels upon a set of targets through an optimization technique. An experimental setup has been designed, built and used to gather data regarding the influences of five light sources upon three targets. The optimization problems corresponding to different lighting demands were solved with the recently introduced Competition Over Resources bio-inspired metaheuristic optimization algorithm. Modifications on this algorithm were proposed in order to enhance its performance in terms of convergence speed and computational effort. The modified algorithm was firstly tested with classical benchmark functions, and then with a real time lighting problem. Once algorithm performance improvements were verified, the energy consumption over a scheduled lighting demand was measured under different test conditions as to evaluate potential savings whilst minimizing the computational cost of running the algorithm. Using the proposed modifications, the best parameters for each lighting demand condition in the predefined schedule have been identified. By using these parameters along with a strategy of reusing the best solution while the lighting demand does not change, smaller population sizes in the algorithm could be used. Finally, the minimum algorithm population size at which energetic performance begins to be affected was identified. Promising results in terms of energy efficiency and reduced computational effort were achieved. [ABSTRACT FROM AUTHOR]

Large-space buildings have large spatial scale, complicated structures and dense people. Roof systems are key parts of their enclosures. The structural systems of large-space buildings need light materials and reasonable stresses while cover soils suggest load increase and structural bearing capacity adaptability change. In this paper, comprehensive adaptability of the overall cover soil of saddle-shaped shell structures and energy efficiency are simulated and evaluated through Sap2000 software and Design Builder software. The findings are that cover soil greening can greatly save energy for buildings of “saddle-shaped” shell structures and structural thicknesses increase linearly with cover soil load increase, the structures are highly adaptive to cover soil, and it is positively significant to combine large space “saddle-shaped” shell structures with cover soil greening. The integration of the relationship among the “saddle-shaped” shell structure, energy-conserving effect, and cover soil load provides further research basis for the practical application of the overall roof greening of “saddle-shaped” shell structure. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) has been developed to improve the cost and/or energy efficiency of complex heating, ventilation and air-conditioning (HVAC) systems. In current literature, almost all of the developed real-time optimization methods belong to the type of time-driven optimization, in which the action of optimization is triggered by “time”. As optimization should be done when the system operating conditions experience a change that is large enough to cause current operational setting not optimal any more, optimization strategies should recognize ‘significant’ changes and perform optimization when necessary. Since the time-driven optimization is a periodic mechanism in nature while those ‘significant’ changes may not be periodic, the time-driven optimization cannot capture ‘significant’ changes and do the optimization promptly. Therefore, this paper proposes an event-driven optimization (EDO) for complex HVAC systems, the key idea of which is to use “event” rather than “time” to trigger the action of optimization. A systematic event-driven optimization method is illustrated, where its main tasks, including event definition and event identification, will be discussed. The proposed method will be compared with a conventional time-driven method using case studies, through which the main advantages of the proposed method will be identified. [ABSTRACT FROM AUTHOR]

A segmented dynamic dwelling stock model is proven useful for understanding the development and changes of ageing building stocks, which is highly relevant for renovation measures and estimates of energy use and emissions in aggregated building stocks. In this paper, such a model is developed further for detailed analyses of dwelling stock energy demand and exemplified for the Norwegian dwelling stock 1960–2015. The dwelling stock model simulates the development in stock size and composition and is combined with archetype-specific energy intensities to estimate the total energy demand. After calibrating the model results with statistics, the model is used to explore the phenomena and causes of historical changes. A large-scale improvement of the energy efficiency of the Norwegian dwelling stock has taken place through renovation and construction of new dwellings. A historical shift to more efficient energy carriers and heating systems has had an effect on energy savings in the system, of the same size as the effect of the improved energy efficiency of the stock. However, the total average energy savings per m 2 are offset by changes in user heating habits. A significant decrease in average delivered energy intensity per m 2 is only observed after the introduction of heat pumps. [ABSTRACT FROM AUTHOR]

The Spanish residential building stock built between 1940 and 1980 period presents a high energy saving potential as it represents a significant percentage of the total housing stock and it was built without any regulation regarding energy efficiency. Accordingly, the first aim of this paper was to quantify the energy saving potential and the related reduction in CO 2 emissions of the housing stock in those years. The work focuses on the Mediterranean climate of the Comunitat Valenciana Region, therefore the energy efficiency strategies proposed are passive and deal with the envelope of buildings. On the basis of the results obtained, several scenarios with intervention periods of 10, 20 and 30 years were calculated. The conclusion was that with the current rate of annual building renovation, it will be nearly impossible to reach the goals established by the EPBD. Only a deep transformation of the building fabric within this sector could help in achieving them. Finally, it should be noted that the proposed methodology can be applied to other areas with different climates, particularly in Spain. [ABSTRACT FROM AUTHOR]

The objective of this article is to improve diagnosing and data availability for all stakeholders, including building operators, planners, inhabitants and utility suppliers. This paper presents the results concerning the demand for final and primary usable energy. For each of the buildings a preliminary energy audit was prepared and this was the basis for comparison between the estimated energy consumption in the buildings before and after thermal upgrading. The research was carried out mainly in buildings belonging to the Department of Public Utilities and Housing in Zielona Góra. The group of selected buildings consisted mainly of traditional technology buildings having different functions, i.e. residential, residential-commercial, commercial, and other buildings (schools and boarding houses). Forty buildings were selected for analysis. They were built in different years and had different functions, i.e.: 23 residential buildings, 6 residential commercial buildings, 7 commercial buildings, and 4 buildings had other functions (schools). They are the main energy consumers, which has a considerable impact on the environment. [ABSTRACT FROM AUTHOR]

Vernacular architecture is closely and traditionally linked to energy efficiency due to its adaptation to climate and location. The main purpose of this paper is to diagnose the hygrothermal behaviour inside the vernacular housings of the Jerte Valley ( Valle del Jerte) , located in the Spanish Central System mountain range ( Sistema Central ). This region is widely characterised by a Mediterranean continental mountain climate with medium-sized mountains and presents two distinguished six-month periods: one is warm and dry, whereas the other is cold and rainy. The objective of the aforementioned diagnosis is to promote the preservation of buildings and its energy refurbishment. As a starting point, a study that associates the region’s monthly climate data with the thermal comfort has been developed. Afterwards, a regional building type has been defined for further use by an energy simulation program in order to measure the hygrothermal behaviour of the envelope. Finally, these results have been related both to thermal comfort and bioclimatic strategies. The results suggest that, during the warm period, indoor conditions are comfortable without the need for an additional energy supply. Nevertheless, in the cold period, indoor conditions are warmer than outdoor conditions, but an additional external energy supply will be required to achieve the comfort zone. Accordingly, it is determined that the refurbishment solutions used for those constructions must maintain intact the bioclimatic strategies that benefit summer conditions. However, strategies that give rise to winter conditions without changing its summer behaviour must be improved. [ABSTRACT FROM AUTHOR]

Non-residential buildings like healthcare ones contribute to energy consumption and cause a negative environmental impact. This is mainly due to the age of the buildings, their poor level of energy efficiency and the implementation of only a basic maintenance plan. Owing to the very limited budget available for public administrators, Energy Performance Contracting (EPC), that entails the involvement of an Energy Service Company (ESCo), can provide the entire or part of the capital needed for investments aimed at progressively increasing energy efficiency over their service life. In this paper, three acute hospitals and two community clinics built in Italy are analysed, in order to assess the economic feasibility of several energy renovation actions that can be included within EPC contracts. To this purpose, the outcomes of energy audits carried out in 2014 about these buildings are reported, which involved analyses of consumption measured over the previous three years and assessment of use profiles for the development of models to break down the overall consumption and to estimate potential savings. Diverse improvement strategies were assessed, consisting in either isolated or combined renovation actions and tested on the five buildings, whose comparison was carried out in terms of energy saving, pollution reduction and economic feasibility. As a result, the best combined renovation actions per each hospital were recommended, that included – but were not limited to – the improvement of the control and regulation of existing sub-systems without replacement, partial replacement of other sub-systems and integration of renewables. Finally, each scenario was evaluated through the payback period and other economic performance indexes in order to assess the feasibility of the EPC framework when applied to this case study. [ABSTRACT FROM AUTHOR]

Evaluation methods can be used to determine what constitutes good energy performance in a building. With an increasing focus on energy use of buildings worldwide, this evaluation assumes a fundamental importance. This paper provides a comprehensive review of the available methods for analysing, classifying, benchmarking, rating and evaluating energy performance in non-domestic buildings. Methodologies are grouped in five categories: engineering calculations, simulation, statistical methods, machine learning and other methods. Techniques for evaluating buildings are described, their principal applications are shown and limitations are identified. The use of performance evaluation in energy efficiency programmes and standards is mapped. There is a need to further develop interactions between the main modelling techniques to produce simple, robust and validated models. Also, evaluation techniques must be developed to consider comfort or service provision in the buildings as a factor in energy performance. [ABSTRACT FROM AUTHOR]

The effective and efficient utilization of electrical energy is the outcome of smart energy management system. Moreover, appropriate augmentation of renewable energy alternatives through distributed generation can considerably unburden the power utility system. This paper identifies the scope for the rule based approach to develop an energy efficient utilization model for bulk power consumers. The augmentation of distributed power generation using solar PV has also been proposed which considerably reduces the maximum demand (M.D) of power during peak hours. The distributed generation from solar PV for this case-study uses minimum storage battery as the generation schedule and the load schedule often coincides. The portfolio of energy distribution is formulated based on priority of energy demand. The proposed model can be suitably modified for similar type of load centers including industrial loads. [ABSTRACT FROM AUTHOR]

Energy consumption in buildings is a key factor when accomplishing national and global CO 2 emission goals. The economic savings per building are limited and are difficult to argue. However, the efforts can be aggregated on district or city level: the approach described in this paper is a large scale co-simulation environment that helps to leverage efficiency by providing quantitative information about the efficiency impact on large scale, while minimizing the efforts in modeling single buildings. It addresses the challenge of modeling thermal behavior of a large neighborhood of buildings in order to calculate energy demands, regarding weather conditions, shading and room temperature settings with a time resolution below one hour. A proof of concept applies the method to a demonstration site of residential buildings and shows the heating demand and CO 2 footprint of the neighborhood. [ABSTRACT FROM AUTHOR]

Abstract: This paper compares several different simplified methodologies for building energy performance assessment during winter time selected based on its large application and/or its user friendly characteristics. Hourly values for energy demand are calculated by a detailed comparison tool based on hourly weather data. A simplified building procedure spreadsheet to evaluate energy demand is also presented. It gives reliable results, needs only a little input data to describe the building design and low time consumption. The spreadsheet is therefore useful for preliminary design tasks in the early design stages where rough estimates of the building design and preliminary energy use are needed for primary decision support. The method is based on a seasonal heating calculation of heat losses and usable heat gains for the building, where the heat-accumulating capacity of the building is considered. The seasonal mean values of the solar radiation and degree days are applied. This paper describes the applied calculation technique and the assumptions made to carry out heating calculation. [Copyright &y& Elsevier]

• A new methodology to investigate the impact of thermal mass is defined. • The use of unrepresentative boundary conditions could lead to inaccurate conclusions. • High thermal mass helps to decrease and regulate the internal temperature. • High thermal mass could increase energy use in the summer and winter seasons. • Isolating thermal mass through internal insulation results in better energy performance than external insulation. Contradictory conclusions have been reported on whether thermal mass reduces or increases energy demand in hot and cold climates. A novel approach to measuring the impact of thermal mass on the building's energy consumption is presented. The method aims to better represent reality and applies two different control conditions simultaneously in a numerical model: the first condition, which has been used in past studies, adjusts the internal boundary condition based on the time duration of heating/cooling activation (simulating an occupancy pattern) and the second, which has not been used in past studies, adjusts the internal boundary condition based on both the time duration and the internal temperature (simulating an occupancy pattern and the thermostatic control system). When investigating the impact of thermal mass on the internal temperature in a scenario where no heat is transferred into the building, the high thermal mass outperforms the low thermal mass, showing a time-shifting effect, long time lag, and small decrement factor. However, when modelled using the method presented in this paper, high thermal mass results in greater energy consumption during summer and winter periods in Saudi Arabia. During transition seasons where temperature swings occur on either side of the indoor set-point temperatures, thermal mass reduces thermal transmission. The impact of thermal mass is also investigated for several climatic conditions in Saudi Arabia. Simulation results show that an internally exposed thermal massive wall section results in greater energy consumption when compared to a similar building with isolated thermal mass (i.e. insulated internally). This is due to the fact that even though the internal temperature inside the building is being cooled/warmed up to the set-point, the thermal mass continues to transfer heat that is absorbed by the building's fabric from/to the interior, placing a greater demand on the cooling/heating system. Over the course of a year, however, the impact of thermal mass is negligible when compared to other factors, such as the insulative properties of the building's fabric. [ABSTRACT FROM AUTHOR]

Occupancy-Centric Controls (OCC) are specialized control sequences which modulate building operation as a function of the sensed occupancy status, thereby leading to increased energy savings, while maintaining thermal comfort. However, occupancy sensor nonidealities, such as bias, latency, random noise, or misdetection, can degrade the performance of OCCs. In this paper, we perform a systematic simulation-based investigation to understand the impacts of nonidealities from both occupancy counting and presence sensors on OCCs. We use a prototype medium office EnergyPlus-based building model, with a detailed zoning layout, as our evaluation testbed. Subsequently, we evaluate the effectiveness of rule-based, occupancy-centric HVAC, lighting and equipment control strategies on an annualized basis. Furthermore, we systematically characterize occupancy sensor nonidealities. Our results indicate that for counting sensors, although the effects of random noise on energy consumption and thermal comfort are minimal, sensor bias and latency can increase HVAC and whole building energy consumption by up to 3.3 % and 4.9 %, respectively. Additionally, noting that an enumeration approach to understand the sensor impact evaluation map for all considered nonidealities may be resource- and time-intensive, we propose a Bayesian Optimization (BO)-based smart sampling approach to efficiently identify the most impactful sensor nonideality sets. Our results indicate that the proposed BO-based approach is able to increase the computational efficiency by 60-fold, when compared to an exhaustive grid-search (enumeration) method. For presence sensors, we observed that higher false positive rates could have a direct impact on HVAC and whole building energy consumption. A low-grade presence sensor could lead to the increase of HVAC and whole building energy consumption by 12.5 % and 8.1 % on average. [ABSTRACT FROM AUTHOR]

This paper studies the effects of energy efficiency measures on energy demand in the Krsan Castle, which is a cultural heritage complex in Istria (Croatia). The heating and cooling energy demand is calculated by means of a dynamic building energy model based on hourly weather data. The largest individual reductions are obtained with thermal insulation for the heating demand, and with external window shading and natural night ventilation for the cooling demand. Energy reductions of up to 80%, relatively to the existing state, can be achieved by energy retrofit projects which include thermal insulation, energy efficient windows, improved airtightness, mechanical ventilation with heat recovery and passive cooling techniques. The economic analysis revealed that payback periods between 10 and 16 years can be achieved by this kind of energy retrofit projects. [ABSTRACT FROM AUTHOR]

The total energy demand of data centres has experienced an important increase in the last years. This is why data centres industry and researchers are working on implementing energy efficiency measures in data centre portfolio. The cooling system of these unique infrastructures can account for 40% of the total energy consumption. Therefore, air management plays an important role to reduce operating costs and to increase overall cooling system efficiency. In order to improve and optimize the energy consumption and thermal management in data centres, appropriate metrics are imperative to evaluate their efficiency and performance. The present paper examines the air management of a real data centre by the use of the most promising air metrics. Noticeable air recirculation and hot spots were observed in the facility. Dynamic modelling was used to evaluate the benefit of air inefficiency mitigation. The combination of air management improvement with increasing the supply air temperature and reducing the air flow rate would allow important energy reduction in the cooling system. [ABSTRACT FROM AUTHOR]

In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods. [ABSTRACT FROM AUTHOR]

Ground source heat pump systems have been proved to be one of the most efficient systems for heating and cooling in buildings. However, an optimal energy performance depends on a good control of the auxiliaries, which stand for an important part of the total energy consumption. The authors previously developed an experimental in situ optimization methodology for the water circulation pumps frequency of ground source heat pump systems when single stage and multi-stage ON/OFF regulation is employed. However, the user comfort was not completely met under extreme weather conditions during summer. This paper presents the upgrading of this energy optimization strategy combining circulation pumps frequency variation and building supply temperature compensation in order to ensure the user comfort while keeping high energy savings. Experimental results show that the user comfort is met by means of this new methodology and the energy savings (33%) are even higher than those obtained with the previous methodology. [ABSTRACT FROM AUTHOR]

Mathematical models can be used to evaluate the health impacts of housing energy efficiency interventions. However by their nature, models are subject to uncertainty and variability, which are important to quantify if used to support policy decisions. Models that are used to assess the impacts on health of housing energy efficiency interventions are likely to be based on a pair of linked component models: a building physics model which calculates changes in exposures and whose outputs then feed into a health impact model. Current methods to propagate uncertainty in a series of models, where the outputs of one model are inputs to another, invariably use Monte Carlo (MC) numerical simulation. In this paper, two methods are used to quantify the uncertainty in the impact of draught proofing on childhood asthma: the MC simulation method and a semi-analytical method based on integral transforms. Both methods give close results but it is argued that the semi-analytical method has some advantages over the MC method, particularly in quantifying the uncertainties in the main outputs of the building physics model before propagating them to the health model. [ABSTRACT FROM AUTHOR]

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs. [ABSTRACT FROM AUTHOR]

Passive Houses are buildings which provide comfortable indoor conditions at an extremely low heating and cooling load. The peak daily average heating and cooling loads are typically below 10 W/m 2 and annual useful energy demands are below 15 kW h/(m 2 a). The Passive House standard was originally developed in Germany. In this paper we show by hygro-thermal dynamic simulation that it is possible to realize residential Passive Houses in all of the world's relevant climate zones, represented here by Yekaterinburg, Tokyo, Shanghai, Las Vegas, Abu Dhabi, and Singapore. The window quality, insulation levels, and mechanical services all depend on the climate as well as on the building's shape and orientation. The resulting annual energy demand for space conditioning of the Passive Houses is 75 to 95% lower than that of a traditionally insulated building of the same geometry. In humid climates like Shanghai or Singapore, special attention must be paid to humidity aspects. In climates which are hot and humid all year long, the total useful energy demand for sensible and latent cooling may exceed 70 kW h/(m 2 a) even in a Passive House. Finally, it is shown that the architectural quality is not compromised by the Passive House requirements. [ABSTRACT FROM AUTHOR]

• Miscellaneous loads are a major opportunity to save electricity in vacant buildings. • A method for inferring vacancy was developed. • The model generates a continuously updated likelihood of vacancy. • The vacancy inference model requires less ground truth data. • The sensor fusion approach is robust and enables flexibility. Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively). [ABSTRACT FROM AUTHOR]

EN ISO 13790 is the international standard used for the calculation of the thermal energy requirement of buildings. The evaluation of solar gains through the glazed surfaces is done with a simplified procedure that determines the transmitted solar energy through the transparent surfaces as the product of the normal incidence solar gain coefficient g gl,n and a correction factor F W , that considers the directional aspects of the beam solar radiation. In the absence of adequate national guidelines, the correction coefficient F W in EN ISO 13790 for non-scattering glazing is set constant and equal to 0.90. In this paper, the results obtained by the TRNSYS computer code demonstrate that a generalized use of a constant value of F W = 0.90 provides significant deviations. Therefore, a correlation to evaluate a variable F W coefficient, in function of the incidence angle of solar radiation and of the number of glass panes, is reported. The accuracy of the obtained results was verified comparing the calculated values with those provided by the TRNSYS software. Finally, with reference to two buildings located in two localities with different weather data, the thermal energy needs by applying the EN ISO 13790 procedure with F W = 0.90 and with variable F W have been compared. [ABSTRACT FROM AUTHOR]

This paper discusses the maintenance plan optimization problem for the energy efficiency purpose in the building energy efficiency retrofitting context. A subproblem namely the Building Retrofitted Facilities Corrective Maintenance Planning (BRFCMP) problem is proposed, where the corrective maintenance for malfunctioning retrofitted items are involved. The aggregate performances of the homogeneous retrofitted item groups, instead of the individual item performances, are the main consideration of the optimization issue. An aggregate population level optimization model is proposed to address the BRFCMP problem. When further taking into account the uncertainties, the optimization problem is cast into an optimal control problem to reduce the consequent adverse impact, given the dynamic nature of the aggregate performances of the item groups during operation. Both the optimization and control system approaches are applied to solve the BRFCMP problem without or considering uncertainties. An actual building retrofitting project is used as the case study to investigate the important role of maintenance to the building energy efficiency. The effectiveness of the proposed approaches is verified by simulation results. [ABSTRACT FROM AUTHOR]

A deep three-years survey of the existing social housing stock in Emilia Romagna, Italy, has been completed helping to accomplish a full acknowledgement of more than 70 buildings in terms of both their envelopes and heating system characteristics. Analysing the outcome data, a simplified parametric calculation protocol has been created to operate a preliminary audit and energy retrofit simulation on the entire social housing stock of the Region. The results of the study, initial data about the energy state-of-the-art of the building cluster and feasible retrofit solutions, are useful to identify where to focus for a further and more accurate energy diagnosis. This paper represents a synthesis of a three-years research work, demonstrating the potential of the parametric assessment method in terms of evaluating environmental and energy benefits, deriving from scheduled retrofit actions, to assist technicians in endeavouring interventions priority. [ABSTRACT FROM AUTHOR]