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• Artifact conservation and occupant comfort in a fourteen-century church. • Design of microclimatic control system in historical buildings. • CFD simulations and BES for the provision of indoor microclimatic conditions. • Experimental and numerical approaches for the evaluation of indoor microclimate. Heritage buildings represent part of a city's identity, and their conservation, as well as the preservation of the works of art they house, is indispensable. This study gives a contribution to the topic of indoor microclimatic analysis and control in building heritage, focusing on the environmental monitoring of an ancient church in the center of Naples (Italy). The measurement campaign, performed during two typical winter and summer periods, was a necessary preliminary step to examine the current indoor microclimate and to calibrate a dynamic energy model of the church. The numerical model was employed for coupled analyses of the church by performing BES (Building Energy Simulations) and CFD (Computational fluid dynamic) simulations. The monitoring revealed the inadequacy of current indoor microclimatic conditions for the conservation of the artistic heritage, and for the comfort of the occupants, therefore a heating and cooling system was designed paying attention to the historical/artistic value of the church. The results of BES and CFD simulations demonstrate an indoor temperature increase of up to 4.4 °C in winter and a decrease of up to 3 °C during the summer, by assuring the preservation of artifacts (e.g., too cold or too hot conditions can induce cracks of wooden materials) and improving occupants' thermal comfort. [ABSTRACT FROM AUTHOR]

[Display omitted] • Accurate building energy simulations need high modeling and computational burden. • ANN-based MPC is performed through a genetic algorithm for a nearly-zero energy building. • Multi-objective optimization of heating cost and thermal comfort is obtained. • Optimal control strategies for HVAC systems are achieved through MPC. • High benefits in terms of both heating cost and thermal comfort are proved. Using a simulation- and optimization-based framework that leverages artificial neural networks for the model predictive control of space heating operation, machine learning techniques are explored to predict the energy performance of a real nearly zero energy building located in Benevento (Southern Italy, Mediterranean climate). The framework's goal is to minimize heating energy costs and thermal discomfort by providing optimal values of setpoint temperatures on a day-ahead planning horizon based on weather forecasts. To achieve this, a Pareto multi-objective approach is applied, which considers thermal comfort via the adaptive theory of ASHRAE 55, assessing a comfort penalty function. The optimization problem is solved using a genetic algorithm in the MATLAB® environment. Objective functions are evaluated using the coupling between MATLAB® and EnergyPlus, which is used as building performance simulation tool. Multi-criteria decision-making is performed to select an optimal solution from the Pareto front while setting a limit to the comfort penalty. To test the framework, EnergyPlus weather data is used to simulate weather forecasts for typical days of the winter season. Two different economic scenarios are simulated in order to be able to analyze both conditions and given the variability of the electricity price. For the different days tested, the best results in terms of savings achieved through the proposed solution are on Feb. 28, corresponding to the coldest day and the highest energy cost, with daily savings of 26% compared to a reference control with a fixed setpoint of 21 °C, while maintaining similar comfort performance. The results suggest that optimized control is a crucial aspect of sustainable building design, and the proposed framework can be virtually implemented or integrated into automation systems for real-time model predictive control. [ABSTRACT FROM AUTHOR]

The paper proposes a multi-objective optimization approach to address the energy design of the building envelope. A genetic algorithm (GA) is implemented by means of the coupling between MATLAB® and EnergyPlus to minimize primary energy consumption (PEC), energy-related global cost (GC) and discomfort hours (DH). The design variables concern the set point temperatures, the radiative properties of plasters, the thermo-physical properties of envelope components, the window type, the building orientation. The GA performs a Pareto optimization and finally two optimal solutions are recommended: the nZEB (nearly zero energy building) optimal solution, which minimizes PEC, and the cost-optimal solution, which minimizes GC. These solutions provide the optimal design strategies for the public and private stakeholders, respectively, which represent the main actors involved in building design. The approach is applied for the design of a new typical Italian residential building. Four locations are considered to investigate the typical Italian climates. The outcomes can give precious indications to rebuild the Italian residential stock with a view to energy-efficiency and cost-optimality, given that the optimal solutions provide low values of PEC – between 62.0 and 91.9 kWh p /m2a – and of GC – between 456 and 665 €/m2 – depending on the location. Image 1040241 • The methodology optimizes envelope energy design by using a genetic algorithm. • Primary energy consumption, global cost and discomfort hours are minimized. • The optimal most sustainable (nZEB) and the cost-optimal designs are achieved. • The methodology is applied to four different Italian climatic zones. • Original indications to rebuild the Italian residential stock are provided. [ABSTRACT FROM AUTHOR]

The cost-optimal analysis provides the most cost-effective energy retrofit solutions. This can strongly support the deep renovation of buildings. However, an outstanding question arises: How to achieve the robust assessment of cost-optimal solutions, feasible for any building? The paper answers this question by proposing a new multi-stage framework for c ost-optimal a nalysis by multi-objective optimi s ation and a rtificial neural networks, called CASA. It couples EnergyPlus and MATLAB ® . A genetic algorithm allows to select recommended retrofit packages by minimizing energy consumption and thermal discomfort. Among these packages, the cost-optimal solution is identified. It is robust because the algorithm explores a wide domain of retrofit scenarios. The optimization procedure uses artificial neural networks to predict building performance. Large-scale uncertainty and sensitivity analyses are conducted to support the generation of the networks. These latter are tested against data provided by current literature with excellent results. The networks’ applicability to whole building categories and rapidity of evaluation make the procedure feasible for any building. For demonstration, CASA is applied to a reference office building located in South Italy, by investigating the related category. The achieved cost-optimal solution produces global cost savings around 42.4 €/m 2 , and significant reductions of energy consumption, discomfort hours and polluting emissions. [ABSTRACT FROM AUTHOR]

Building activity is the sector that affects for the most part the anthropogenic climate change. Indeed, even if differences can be found among countries, according to the level of development and climates, buildings require about 30–40% of the overall energy demand, with similar share concerning the greenhouse emissions. According to the more recent EU Directives in matter of energy efficiency in energy use, a great attention has to be paid to energy refurbishments of existing buildings. Indeed, the turnover rate of the EU building stock is generally low in fully-developed countries, so that the energy retrofits are also more important compared to new nearly zero-energy buildings. The proposed investigation concerns the demonstrative role of the public hand, whose necessity is underlined by the EU Directives 2010/31/EU and 2012/27/EU, through a multi-step and multi-objective optimization of an educational building of an Italian University. All preliminary investigations aimed at a reliable modelling, the iterative method that combines genetic algorithms and transient energy simulations tailored on calibrated numerical models make the investigation complete and repeatable. All levers of energy efficiency have been pressed, and thus the refurbishment of building envelope, HVAC systems, integration of energy supply by renewables. The multi objective optimisation concerns costs, incentives, indoor comfort, energy demands for heating and cooling and a novel approach is proposed for choosing the best configuration of retrofit. It is resulted that the most profitable energy efficiency measures involve the modernization of energy systems, even if also the retrofit of the building envelope can be profitable under favourable financial conditions. The cost-effective refurbishment reduces the primary energy demand up to a value of 12 kWh/m 2 a, so that the building can be surely considered as nZEB. [ABSTRACT FROM AUTHOR]

The issue of the summer indoor overheating in modern buildings is investigated. Indeed, mainly for heating dominated climates, usually there is a great attention to the design of measures for reducing the energy losses in winter, while the summer comfort often is secondary or neglected. On the other hand, just where the warm season is not so hard, passive energy measures have to be deeply exploited, in order to avoid the installation of air-conditioning systems, with positive effects on both the building and city scales (e.g., the reduction of urban heat islands). After a general overview of the overheating phenomenon, a case report is proposed, where, even if the summer ambient conditions are not extreme, thermal discomfort has been registered during the first years of operations. The office building, recently built in Berlin, fulfils the German requirements of energy efficiency and heat protection in summer. It is not equipped with mechanical systems for the space cooling. According to the new international approach of the cost-optimality, this study investigates strategies for improving the indoor conditions – evaluated by means of the adaptive criteria – during the cooling season, by analyzing various solutions for reducing the heat gains. By means of calibrated numerical models, validated by comparisons with measurements of indoor air temperatures (free-running building), transient energy simulations have been performed. The target of investigation is the research of the optimal solution of solar shadings (i.e., typology and management), in order to find the best compromise in terms of thermally comfortable hours, use of artificial lighting, operational and investment costs. [ABSTRACT FROM AUTHOR]

How to predict building energy performance with low computational times and good reliability? The study answers this question by employing artificial neural networks (ANNs) to assess energy consumption and occupants' thermal comfort for any member of a building category. Two families of ANNs are generated: the first one addresses the existing building stock (as is), the second one addresses the renovated stock in presence of energy retrofit measures (ERMs). The ANNs are generated in MATLAB ® by using the outcomes of EnergyPlus simulations as targets for networks' training and testing. A preliminary ‘Simulation-based Large-scale sensitivity/uncertainty Analysis of Building Energy performance’ (SLABE) is conducted to optimize the ANNs' generation. It allows to identify the networks' inputs and to properly select the ERMs. The developed ANNs can replace standard building performance simulation tools, thereby producing a substantial reduction of computational efforts and times. This can allow a wide diffusion of rigorous approaches for retrofit design, which are currently hampered by the excessive computational burden. As case study, office buildings built in South Italy during 1920–1970 are investigated. Comparing the ANNs' predictions with EnergyPlus targets, the regression coefficient is between 0.960 and 0.995 and the average relative error is between 2.0% and 11%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Simulation- and optimization-based model predictive control is implemented. • A nearly zero energy building in Mediterranean climate is the case study. • Energy cost & discomfort – Fanger & adaptive – are minimized via a Pareto approach. • Cooling setpoints are optimized on a daily horizon based on weather conditions. • Optimal solutions can reduce both cost & discomfort vs a fixed setpoint strategy. The space conditioning of buildings is liable for more than 10 % of World final energy uses and related CO2-eq emissions. Such share must be drastically reduced to pursue sustainability by optimizing both energy design and devices control. In this frame, space cooling is assuming an increasing weight owing to climate change. Accordingly, this study applies a simulation- and optimization-based framework for the model predictive control (MPC) of space cooling systems. The case study is a nearly zero energy building located in Benevento – Southern Italy, Mediterranean climate – featuring an efficient air-source multi-split system for cooling. The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. Accordingly, a Pareto multi-objective approach is applied considering different discomfort indicators to compare the Fanger theory with the adaptive one of ASHRAE 55. The optimization problem is solved by running a genetic algorithm – variant of NSGA II – under MATLAB® environment. The objective functions are assessed via the coupling between MATLAB® and EnergyPlus, using a validated building energy model. The multi-criteria decision-making is performed by setting a limit to discomfort to pick an optimal Pareto solution. The framework is tested addressing a typical day of the cooling season and using monitored weather data to simulate weather forecasts. Different optimal solutions are provided to fit different comfort categories. Compared to a reference control at fixed setpoint – 26 °C – the proposed solutions with similar comfort performance ensure cost savings around 28 %. Besides the proposed hypothetical implementation, the framework can be integrated in automation systems for real-time MPC. The novel contributions of this study lie in the methodology to combine MPC with different thermal comfort models as well as in the results, which provide deeps insights about the application of MPC for the space cooling of nearly zero energy buildings in a balanced climate. [ABSTRACT FROM AUTHOR]

Efficient HVAC devices are not sufficient to achieve high levels of building energy performance, since the regulation/control strategy plays a fundamental role. This study proposes a simulation-based model predictive control (MPC) procedure, consisting of the multi-objective optimization of operating cost for space conditioning and thermal comfort. The procedure combines EnergyPlus and MATLAB ® , in which a genetic algorithm is implemented. The aim is to optimize the hourly set point temperatures with a day-ahead planning horizon, based on forecasts of weather conditions and occupancy profiles. The outcome is the Pareto front, and thus the set of non-dominated solutions, among which the user can choose according to his comfort needs and economic constraints. The critical issue of huge computational time, typical of simulation-based MPC, is overcome by adopting a reliable minimum run period. The procedure can be integrated in building automation systems for achieving a real-time optimized MPC. The methodology is applied to a multi-zone residential building located in the Italian city of Naples, considering a typical day of the heating season. Compared to a standard control strategy, the proposed MPC generates a reduction of operating cost up to 56%, as well as an improvement of thermal comfort. [ABSTRACT FROM AUTHOR]

The recast version of the Energy Performance of Buildings Directive (2010/31/EU) proposes a comparative methodology aimed at defining the energy performance of buildings “with a view to achieving cost-optimal levels”. Really, how can the cost-optimal technologies be detected? Moreover, how can the most proper packages of energy efficiency measures be chosen in order to obtain the cost-optimality? This paper would provide answers to these questions, by proposing a new methodology for the evaluation of the cost-optimality, by means of the multi-objective optimization of energy performance of buildings and indoor thermal comfort. The optimization procedure is developed by means of the coupling between MatLab and EnergyPlus, by implementing a genetic algorithm, and it allows the evaluation of profitable and feasible packages of energy efficiency measures applied to buildings. Then, following the adoption of these packages, the global cost over the lifecycle of the building is calculated, in order to identify the cost-optimal solution. After the presentation of the methodology, the developed method is proposed for the optimization of the energy retrofit of an existing building. Furthermore, the method can be applied also for new architectures, by considering reference buildings. [ABSTRACT FROM AUTHOR]

Highlights: [•] Building envelope refurbishment by means of PCM plaster has been investigated. [•] The analyzes vary melting temperature, thickness and location of PCM plasters. [•] The reduction of cooling need is evaluated for five Mediterranean climates. [•] Masonry-insulated buildings have been studied in realistic operating conditions. [•] Guidelines are proposed for optimizing energy savings by means of proper PCM. [ABSTRACT FROM AUTHOR]

Abstract: The study investigates a critical application in the field of energy demand for air-conditioning: the health care facilities. These buildings are quite energy-intensive, because of necessity of high microclimatic control, need of numerous air changes and, contemporarily, strict set points required for temperature and relative humidity. The achievable energy savings by improving the thermal–physical characteristics of the building envelope are examined, with reference to a medium-size hospital, located in Mediterranean climate. The indoor comfort conditions have been analyzed as well as the reduction of energy demands, depending on the HVAC system. Indeed, the active plants are characterized by quite different energy demands, due to different achievable quality of microclimatic control and indoor air. The refurbishment of the building envelope, as demonstrated, is always convenient, by considering all possible point of views, and thus energy savings, better indoor microclimate, reduction of polluting emissions, technical and economical feasibility. Finally, even if ventilation loads induce the highest heating and cooling demands, however the envelope refurbishment is an effective retrofit action. [Copyright &y& Elsevier]

Abstract: In the museum environment, the temporal stability and the spatial uniformity of the indoor microclimatic parameters are necessary primarily for the correct artwork conservation and then for the occupant thermal comfort. Therefore, a HVAC system is usually necessary. This paper above all emphasizes the integration of Building Energy Performance Simulation (BEPS) and Computational Fluid Dynamics (CFD) codes. This coupled numerical approach can provide accurate information about both the HVAC system energy request and the indoor microclimatic control (temporal and spatial distribution of the parameters). Then, a case study concerning a typical museum exhibition room is examined. The annual energy request for three types of all-air system, as well as the temporal variation of indoor temperature and relative humidity, is evaluated by means of a BEPS code. Energy savings can be obtained using a system with desiccant wheel (11%) or enthalpy wheel (9%), compared to the base system. As regards indoor relative humidity control, this is more critical for summer conditions, and the best performance is obtained by the system with desiccant wheel. Then, the spatial microclimatic control is analyzed by means of a CFD analysis, for various thermal load conditions and different air diffusion xequipments. The best performances are obtained by using the swirling (vortex) diffusers; also the perimetrical stripes of slot diffusers show satisfactory performances. Finally, in order to evaluate the ability of air diffusion equipments to assure good ventilation effectiveness, an analysis of the mean age of air is carried out too. [Copyright &y& Elsevier]

Harnessing the appropriate radiative characteristics of buildings' external surfaces is a highly effective way to reduce internal space heating and cooling demand. Building energy-dynamic simulations are created in order to compare various external coatings with reference to a social housing case study in a typical Mediterranean climate (Naples, Italy). An innovative index, the surface factor (SF), is proposed to inform the choice of the most suitable surface coatings as a function of local climatic conditions and thereby reduce overall energy demand. For cities with high solar gain and/or low winter degrees-day (SF > 0.65), high-reflective and/or high-emissive coatings (i.e. 'cool paints') are suitable, while low-reflective and/or low-emissive external coatings are more suitable for cooler climates (SF < 0.4). The simultaneous interactions of wall mass, surface radiative characteristics, and indoor ventilation on the summer cooling energy demands are evaluated and substantial heating or cooling savings can be achieved, particularly for existing buildings. For regions in which outdoor night temperature is lower than the indoor value, high thermal inertia walls are viable only when coupled to night ventilation. Diurnal ventilation coupled to massive walls was found to be energy inefficient. Exploiter les caracteristiques radiatives appropriees des surfaces exterieures des batiments est une maniere extremement efficace de reduire la demande de chauffage et de refroidissement des espaces interieurs. Il est cree des simulations dynamiques des batiments en termes d'energie afin de comparer les divers revetements exterieurs en prenant pour reference une etude de cas dans le logement social sous un climat mediterraneen type (Naples, en Italie). Un indice innovant, le facteur de surface, est propose afin d'aider a choisir les revetements de surface les plus adaptes en fonction des conditions climatiques locales et de reduire de ce fait la demande energetique globale. Pour des villes ayant un apport solaire eleve et/ou des degres-jour bas en hiver (facteur de surface > 0,65), des revetements hautement reflechissants et/ou a fort pouvoir emissif (c'est-a-dire des « peintures froides ») sont adaptes, tandis que des revetements exterieurs faiblement reflechissants et/ou a faible pouvoir emissif sont plus adaptes sous des climats plus froids (facteur de surface < 0,4). Les interactions simultanees de la masse des murs, des caracteristiques radiatives des surfaces et de la ventilation interieure sur les demandes estivales en energie de refroidissement sont evaluees et d'importantes economies de chauffage ou de refroidissement peuvent etre realisees, s'agissant en particulier de batiments existants. En ce qui concerne les regions dans lesquelles la temperature exterieure nocturne est inferieure a la valeur interieure, des murs a haute inertie thermique ne sont viables que s'ils sont associes a une ventilation nocturne. Il a ete constate qu'une ventilation diurne associee a des murs massifs est energetiquement inefficace. Mots cles: enveloppe des batiments physique des batiments surfaces exterieures revetements de surface confort thermique inertie thermique [ABSTRACT FROM AUTHOR]

The global increase in energy needs and environmental awareness for a more efficient energy use have boosted building rehabilitation to decrease energy consumption. The installation of solar control films (SCFs) in buildings with large glazing façades makes it possible to reduce excessive solar gains through the glazing. The main purpose of the work is to assess, with field experimental data, the thermal and luminous performances of double-glazing units with SCFs installed in office rooms, in Lisbon. An experimental campaign was carried out simultaneously in three adjacent offices: one with a highly reflective SCF (external installation), one with a reflective SCF (internal installation) and one without an SCF. The exterior SCF showed the best thermal performance with reductions in the peak indoor air temperature of up to 6.9 and 2.3 °C during the representative days of the heating and cooling periods, respectively, increasing thermal comfort mainly during the cooling period. The interior SCF had a poorer thermal performance since it contributed to solar radiation absorption that is then emitted as heat into the indoor environment, increasing the greenhouse effect of the office. The presence of SCFs reduced the indoor illuminance levels, having a positive impact on thermal comfort and glare reduction in the cooling period. [ABSTRACT FROM AUTHOR]

The Climate Change scenario projected by the IPCC for the year 2050 predicts noticeable increases in temperature. In severe summer climates, such as the Mediterranean area, this would have very negative effects on thermal comfort in the existing housing stock, given the current high percentage of dwellings which are obsolete in energy terms and house a population at serious risk of energy poverty. The main aim of this paper is to generate a predictive model in order to assess the impact of this future climate scenario on thermal comfort conditions in an entire building category. To do so, calibrated models representing linear-type social multi-family buildings, dating from the post-war period and located in southern Spain, will be simulated extensively using transient energy analyses performed by EnergyPlus. In addition, a sensitivity analysis will be performed to identify the most influential parameters on thermal discomfort. The main results predict a generalized deterioration in indoor thermal comfort conditions due to global warming, increasing the average percentage of discomfort hours during the summer by more than 35%. This characterization of the future thermal behaviour of the residential stock in southern Spain could be a trustworthy tool for decision-making in energy retrofitting projects which are so badly needed. To do so, further work is required on some limitations of this model so that different user profiles and typologies can be represented in detail and an economic assessment can be included. [ABSTRACT FROM AUTHOR]

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]

• Control strategy of the heating of an all-air system in an educational building is optimized. • A nearly zero energy educational building in Belgium is used as case study. • Winter monitoring campaign is analysed and a building energy simulation is calibrated. • Climatic control of the boiler causes a decrease of comfort hours up to 23%. • Climatic control of the storage tank determines energy saving until up to 47%. The optimal control of the HVAC system in the nearly zero energy buildings (nZEBs) can be considered as a challenge. Indeed, the lack of data given by monitored data does not allow understanding what are the implications of different strategies on thermal comfort and energy consumptions. This paper determines an approach for the evaluation of the management of the heating system in an existing educational nZEB. The aim is to understand how the energy efficiency of the heating system is sensitive to controls strategies and the important role played by the control systems. The test lecture rooms on KU Leuven Ghent Technology Campus (Belgium) have been chosen as case study. A deep analysis, starting from an in-field monitoring, was carried out during the winter period of 2019, in order to assess the actual operation and the energy use of building systems under real conditions. Then, a building energy simulation (BES) model of the building and its systems was developed and calibrated against the real monitoring data. Control strategies of the heating system are simulated and compared to the current configuration. The simulation results show that strategies based on a definition of a heating curve for the boiler can reduce the comfortable time (−23%). Controlling the temperature inside the storage tank as a function of the outdoor temperature together with the control on the activation of the air handling unit result in an energy savings from 32% to 46%, with a correspondent increase of comfort time in the period from 0.6% to 3.4% compared to the baseline scenario, depending to the relationship between the external temperature and water temperature inside the water storage tank. [ABSTRACT FROM AUTHOR]