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Solar systems based on the coupling of parabolic concentrating collectors and thermal engines (i.e. dish-Stirling systems) are among the most efficient generators of solar power currently available. This study focuses on the modelling of functioning data from a 32 kWe dish-Stirling solar plant installed at a facility test site on the University of Palermo campus, in Southern Italy. The proposed model, based on real monitored data, the energy balance of the collector and the partial load efficiency of the Stirling engine, can be used easily to simulate the annual energy production of such systems, making use of the solar radiation database, with the aim of encouraging a greater commercialisation of this technology. Introducing further simplifying assumptions based on our experimental data, the model can be linearised providing a new analytical expression of the parameters that characterise the widely used Stine empirical model. The model was calibrated against data corresponding to the collector with clean mirrors and used to predict the net electric production of the dish-Stirling accurately. A numerical method for assessing the daily level of mirror soiling without the use of direct reflectivity measures was also defined. The proposed methodology was used to evaluate the history of mirror soiling for the observation period, which shows a strong correlation with the recorded sequence of rains and dust depositions. The results of this study emphasise how desert dust transport events, frequent occurrences in parts of the Mediterranean, can have a dramatic impact on the electric power generation of dish-Stirling plants. © 2019 Elsevier Ltd

Historic and traditional buildings, including rural ones, are a territorial resource in Europe and constitute an integral part of the European’s cultural heritage. However they are often characterized by poor energy performances and a large potential for energy retrofit actions. On the other hand, the hardest part of retrofitting such buildings is the limited invasiveness that such actions need to have on the historical and heritage value of the building itself. The paper describes an experience of re-design of an existing rural building located in Sicily, inside the ancient Greeks “Valley of the Temples”. An energy audit was performed on the building, its energy uses thoroughly investigated. A building model was developed in TRNSYS environment and its performances validated. The validated model was used for redesign studies aimed towards the improvement of the energy performances of the building in compliance to legislation. The best performing solutions to be applied to a case-study like the Sanfilippo House are those regarding the management of the building, as in the case of the natural ventilation and the HVAC setpoints, that would allow a large impact (up to 10% reductions in energy uses) on the energy performances of the building with no invasiveness, and those with very limited invasiveness and high impact on the energy efficiency of the building, as in the lighting scenario (up to 30% energy uses reduction). The most invasive actions can only be justified in the case of high energy savings as in the case of the insulation of the roof, otherwise should be disregarded.

Il contributo descrive il lavoro effettuato dagli operatori dell’USSM di Palermo relativamente ai minori sex offenders. Considerata la specificità dell’azione violenta posta in atto, da alcuni anni l’Ufficio si è dotato di una “task force” interna (gruppo E.O.S.) che sperimenta e implementa modalità di comprensione del fenomeno, presa in carico congiunta, creazione di un modello condiviso di lavoro sul target. Nello specifico, un focus verrà aperto sulle strategie operative principali adottate e sull’attività necessaria, quanto mai faticosa, di intervenire sui meccanismi di negazione dei rei e delle famiglie degli stessi. In questo senso l’approccio con le famiglie appare una “conditio sine qua non” per avviare una migliore rielaborazione dell’evento, il ripristino di una comunicazione più efficace sul background del reato nonché sulla vittima e i suoi vissuti.

Sustainability of energy supply strategies in small islands has been emerging as a severe issue, due to the large margins for improvement and rationalization of the most frequently adopted solutions. In most of the European islands, large amounts of heat are wasted by the operation of engine-based power plants; conversely, heat is produced by boilers (supplied by liquid fuels) or by electric equipment for a number of different uses, like domestic hot water production or space heating in winter. In this paper a techno-economic analysis is proposed to assess the feasibility of CHP-retrofit of the existing power plants and the possible utilization of the recovered heat to supply, via a district heating and/or cooling network, the energy requests of civil energy users (both in the tertiary and in the residential sector). The analysis is accurately performed for six islands located in Italy and characterised by different context conditions from a demographic, geographic and climatic viewpoint, so as to get a comprehensive understanding of the factors that favour/obstruct the economic feasibility of the examined technical solution. As expected, due to the low “linear heat density” usually observed in small islands and to the complex orographic profiles, the investment usually resulted “far from being attractive”; only in the case where public incentive or support mechanism is adopted, the possible integration of the existing power plants with heat recovery devices and a district heating network resulted in moderately attractive, especially in the largest examined islands due to their highest heat loads.

The combination/integration of renewable energy and storage systems appears to have significant potential, achieving high-energy results with lower costs and emissions. One way to cover the thermal needs of a building is through solar energy and its seasonal storage in the ground. The SMARTEP project aims to create an experimental area that provides for the construction of a road solar thermal collector directly connected to a seasonal low-temperature geothermal storage with vertical boreholes. The storage can be connected to a ground-to-water heat pump for building acclimatization. This system will meet the requirements of visual impact and reduction of the occupied area. Nevertheless, several constraints related to the radiative properties of the surfaces and the lack of proper thermal insulation have to be addressed. The project includes the study of several configurations and suitable materials, the set-up of a dynamic simulation model and the construction of a small-scale road thermal collector. These phases allowed for an experimental area to be built. Thanks to careful investigation in the field, it will be possible to identify the characteristics and the best operation strategy to maximize the energy management of the whole system in the Mediterranean area.

In geographical areas where direct solar irradiation levels are relatively high, concentrated solar energy systems are one of the most promising green energy technologies. Dish-Stirling systems are those that achieve the highest levels of solar-to-electric conversion efficiency, and yet they are still among the least common commercially available technologies. This paper focuses on a strategy aimed at promoting greater diffusion of dish-Stirling systems, which involves optimizing the size of the collector aperture area based on the hourly frequency distributions of beam irradiance and defining a new incentive scheme with a feed-in tariff that is variable with the installed costs of the technology. To this purpose, a new numerical model was defined and calibrated on the experimental data collected for an existing dish-Stirling plant located in Palermo (Italy). Hourly-based simulations were carried out to assess the energy performance of 6 different system configurations located on 7 sites in the central Mediterranean area using two different solar databases: Meteonorm and PVGIS. A new simplified calculation approach was also developed to simulate the dish-Stirling energy production from the hourly frequency histograms of the beam irradiance. The results reveal that an optimised dish-Stirling system can produce 70–87 MWhe/year in locations with direct irradiation varying between 2000 and 2500 kWh/(m2·year). The proposed incentive scheme would guarantee a payback time for investment in this technology of about ten years and the effect of economies of scale could lead, over the years, to a levelized cost of energy similar to that of other concentrating power systems.

The power curve of a wind turbine describes the generated power versus instantaneous wind speed. Assessing wind turbine performance under laboratory ideal conditions will always tend to be optimistic and rarely reflects how the turbine actually behaves in a real situation. Occasionally, some aerogenerators produce significantly different from nominal power curve, causing economic losses to the promoters of the investment. Our research aims to model actual wind turbine power curve and its variation from nominal power curve. The study was carried out in three different phases starting from wind speed and related power production data of a Senvion MM92 aero-generator with a rated power of 2.05 MW. The first phase was focused on statistical analyses, using the most common and reliable probability density functions. The second phase was focused on the analysis and modelling of real power curves obtained on site during one year of operation by fitting processes on real production data. The third was focused on the development of a model based on the use of an Artificial Neural Networks that can predict the amount of delivered power. The actual power curve modelled with a multi-layered neural network was compared with nominal characteristics and the performances assessed by the turbine SCADA. For the studied device, deviations are below 1% for the producibility and below 0.5% for the actual power curves obtained with both methods. The model can be used for any wind turbine to verify real performances and to check fault conditions helping operators in understanding normal and abnormal behaviour.

Multiple-chillers systems represent viable solutions for medium/large-scale air conditioning applications characterized by variable cooling demand. The energy efficiency of such systems is influenced by the number of chillers, the combination of cooling capacities, and the load-sharing among the units. Large efforts have been devoted to developing efficient operation strategies for these systems, but rules of thumb are usually adopted for selecting cooling capacities thus leaving room for energy and economic savings. In this paper, exergoeconomic analysis is proposed as a promising method to identify near-optimal design and operation strategies, due to the capability of exergoeconomic indicators to account simultaneously for capital and operating costs. The potential of the method is illustrated for a hydronic system supplying an air-handling unit installed in an office building. Design alternatives are compared, with chillers of equal or different capacities operated in a parallel or series configuration, and the cost-effectiveness of different load sharing strategies is also investigated. A thermoeconomic model for multiple-chillers systems is formulated, considering the actual performance of chillers under full- and part-load conditions derived by a plant simulator. Results show that the exergoeconomic cost of chilled water reduced by about 7% and 30% when passing from evenly to unevenly sized systems in both series and parallel configurations. It is also found that the symmetric load sharing strategy leads to a 14–18% reduction in the cost of chiller water compared to the sequential one. The study confirms that this method may represent systematic and thermodynamically-sound support for engineers in this field.

Optimal design of a solar thermal system is necessary to minimize payback time and to diffuse renewable energy use for Domestic Hot Water production in residential areas. More accurate design is crucial in the case of seasonal or occasional use of the system; indeed, the standard criteria generally applied to a design system for continuous use, can lead to considerable over-sizing. To speed up the design phase and to help the planner in the identification of the best solution without any complex evaluation or long computational time, it would be interesting to have available a simpler method than the standard procedures, but one that is reliable and accurate for the evaluation of the best configuration, taking into account occasional use, seasonal and monthly domestic hot water demand, orientation and primary flow rate. To this end, the authors investigated a methodology for the identification of some empirical correlations based on the analysis of data coming from a parametric simulation; in this way the identified correlations can indicate, with high reliability, the optimal design knowing only well-known parameters. In detail, the data output was extracted and processed to evaluate the best design configurations under any operative conditions. Determination of the best configuration identifies the operative parameters that maximize the Solar Fraction of the plant and minimize the auxiliary energy. To highlight the reliability of this methodology, in this work, the authors describe a case study of the Sicilian region proposing a set of simple, reliable correlations that allow the determination of the best tilt angle for monthly or seasonal use. Following the same steps the procedure can be replicate in any context and in any conditions.

Different ways to evaluate the building energy balance can be found in literature, including comprehensive techniques, statistical and machine-learning methods and hybrid approaches. The identification of the most suitable approach is important to accelerate the preliminary energy assessment. In the first category, several numerical methods have been developed and implemented in specialised software using different mathematical languages. However, these tools require an expert user and a model calibration. The authors, in order to overcome these limitations, have developed an alternative, reliable linear regression model to determine building energy needs. Starting from a detailed and calibrated dynamic model, it was possible to implement a parametric simulation that solves the energy performance of 195 scenarios. The lack of general results led the authors to investigate a statistical method also capable of supporting an unskilled user in the estimation of the building energy demand. To guarantee high reliability and ease of use, a selection of the most suitable variables was conducted by careful sensitivity analysis using the Pearson coefficient. The Multiple Linear Regression method allowed the development of some simple relationships to determine the thermal heating or cooling energy demand of a generic building as a function of only a few, well-known parameters. Deep statistical analysis of the main error indices underlined the high reliability of the results. This approach is not targeted at replacing a dynamic simulation model, but it represents a simple decision support tool for the preliminary assessment of the energy demand related to any building and any weather condition.

Italy is celebrated in the world for its agri-food industries while the process of production of pasta is highly energy demanding and requires both electrical and thermal energy simultaneously. Because most of the Italian factories producing pasta are located in the Southern part of the country, the direct use of thermal energy generated from the sun would be particularly profitable. In this study, the authors examine the possibility of generating by a Solar Industrial Process Heating plant the thermal energy required annually by an existing factory that produces durum wheat pasta located in Sicily (Italy). The hypothesized plant scheme consists of an array of Fresnel linear solar collectors and a concrete thermal energy storage system in which a heat transfer diathermal fluid circulates. This particular combination, although not the most efficient from the thermodynamic point of view, determines a lower visual impact and easier maintenance during the life span of the system. The use of food graded thermal oil ensures a high level of safety. A TRNSYS model has been developed in order to simulate the energy performance of the above described plant with the aim of optimizing the design of the solar heat for industrial process systems in terms of solar collectors and thermal energy storage dimensions taking into account the available space in the specific location. The obtained results show that the direct use of the thermal energy generated with the Fresnel solar collectors can significantly contribute to increase the sustainability of the most thermal energy-demanding factories working in the food industry, a strategic sector in the Mediterranean Area. The average annual solar contribution can reach about 40% of the total thermal energy requirement, maximizing the solar energy production during the summer season. Moreover, the proposed study allowed the determination of the maximum investment cost of the plant linked to a simple payback time, without external incentives, of 8 years.

A detailed assessment of building energy performance requires a large amount of input data concerning building typology, environmental conditions, envelope thermophysical properties, geometry, control strategies, and several other parameters. Notwithstanding, the use of specialized software tools poses many challenges in regards to the retrieval of reliable and detailed information, setting a steep learning curve for engineers and energy managers. To speed up the preliminary assessment phase, it might be more convenient to resort to a simplified model that allows the evaluation of heating energy demand with a good level of accuracy and without excessive computational cost or user expertise. Dimensional analysis is a means of simplifying a physical problem by appealing to dimensional homogeneity to reduce the number of relevant variables. In this work, the authors investigated an alternative approach to assess the thermal energy demand of a high-performance-non-residential building. It was possible to define some dimensionless numbers that synthetically describe the links between the main characteristic parameters of the thermal balance by applying the Buckingham π theorem. After a detailed description of the Buckingham π theorem and of its application concerning the evaluation of the building energy balance, the authors identified nine “ad hoc” dimensionless numbers. The proposed methodology has been validated by the comparison of the heating energy demand calculated by detailed dynamic simulations carried out in TRNSYS according to the standards and laws of building energy requirements in seven different European countries. Applying a set of criteria, it was possible to employ a dimensionless number to determine, immediately and without any calculation or use of steady/dynamic software, the heating energy demand with an reliability >90%.

The public buildings sector represents one of the most intensive items of EU energy consumption; the application of retrofit solutions in existing buildings is a crucial way to reduce its impact. To facilitate the knowledge of the energy performance of existing non-residential buildings and the choice of the more adequate actions, Public Administrations (PA) should have the availability of proper tools. Within the Italian project "POI 2007-13", a database and a decision support tool, for easy use, even to a non-technical user, have been developed. A large set of data, obtained from the energy audits of 151 existing public buildings located in four regions of South Italy have been analysed, elaborated, and organised in a database. This was used to identify the best architectures of two ANNs and to train them. The first ANN provides the actual energy performance of any building; the second ANN assesses key economic indicators. A decision support tool, based on the use of these ANNs is conceived for a fast prediction of the energy performance of buildings and for a first selection of energy retrofit actions that can be applied.

Losses of electricity production in photovoltaic systems are mainly caused by the presence of faults that affect the efficiency of the systems. The identification of any overheating in a photovoltaic module, through the thermographic non-destructive test, may be essential to maintain the correct functioning of the photovoltaic system quickly and cost-effectively, without interrupting its normal operation. This work proposes a system for the automatic classification of thermographic images using a convolutional neural network, developed via open-source libraries. To reduce image noise, various pre-processing strategies were evaluated, including normalization and homogenization of pixels, greyscaling, thresholding, discrete wavelet transform, and Sobel Feldman and box blur filtering. These techniques allow the classification of thermographic images of differen quality and acquired using different equipments, without specific protocols. Several tests with different parameters and overfitting reduction techniques were carried out to assess the performance of the neural networks: images acquired by unmanned aerial vehicles and ground-based operators were compared for the network performance and for the time required to execute the thermographic inspection. Our tool is based on a convolutional neural network that allows to immediately recognize a failure in a PV panel reaching a very high accuracy. Considering a dataset of 1000 images that refer to different acquisition protocols, it was reached an accuracy of 99% for a convolutional neural network with 30 min of computational time on Low Mid-Range CPU. While a dataset of 200 sectioned images, the same tool achieved 90% accuracy with a multi-layer perceptron architecture and 100% accuracy for a convolutional neural network. The proposed methodology offers an open alternative and a valid tool that improves the resolution of image classification for remote failure detection problems and that can be used in any scientific sector.

The weather is one of the main factors to consider when designing a building because it represents the most important boundary condition to affect the dynamic behaviour of the building. In the literature, many studies use the degree day to predict building energy demand. However, linking the results obtained from a generic building simulation tool with defined degree days, will not give reliable energy evaluation. The goal of this study is to demonstrate that the assessment of building energy demand through the use of the degree day is correct only if the determination of the climate index is a function of the same weather data. The relationship between Heating Degree-Day and heating energy performance was identified by determining some simple correlations, in order to obtain a preliminary evaluation of energy demands. The authors used Heating Degree Days based on three climate data-sets, developing different relationships and feedback. For the extraction of these correlations, numerous dynamic simulations on non-residential buildings characterized by high-energy performance were carried out. From the analysis of the results, it is clear that the relationships with higher correlation coefficients (higher than 0.9) are those that are a function of the degree calculated from the same climatic file used during the simulations. The proposed methodology, validated in this work for an Italian case study can be extended to any country and can be used to improve the reliability of any decision support tool based on climatic indexes.

A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the standards and laws of building energy requirements in seven different European countries, for 3 cities in each country and with 13 different shape factors, obtaining 2184 detailed dynamic simulations of non-residential buildings designed with high energy performances. The authors identified the best ANN topology developing a tool for determining, both quickly and simply, the heating energy demand of a non-residential building, knowing only 12 well-known thermo-physical parameters and without any computational cost or knowledge of the thermal balance. The reliability of this approach is demonstrated by the low standard deviation less than 5 kWh/(m2·year).

This article presents a procedure for the producibility curve identification of a dish-Stirling plant, starting from experimental data. The producibility data was measured, recorded, analysed, filtered and monthly aggregated. Moreover, the incidence of the ambient temperature and of the mirrors cleaning on producibility data is highlighted and a procedure to normalize the measured data in temperature and cleaning level was developed. To provide a validation of the developed procedure the producibility curves at 25 °C have been obtained and compared with the one issued by the manufacturer. The two curves are in good agreement, presenting a maximum deviation of the 7 %.

Solar heating and cooling systems are reliable and feasible solutions among renewable energy technologies. Indeed, solar thermal devices help reduce primary energy consumption and can reduce electricity demand, thus representing one of the best options for satisfying heating and/or cooling energy supply. The Borehole Thermal Energy Storage (BTES) represents one of the best promising option among the various storage technologies, because the size of the storage can be easily extended by drilling additional boreholes and simply connecting the pipes to the existing boreholes; the overall energy efficiency of this system is about 40–60%. In this paper, the authors present an application of this technology for the heating system of a school building located in the Southern part of Italy. Two different energy schemes are presented: a school equipped with a conventional gas boiler system with radiators and the same school building with a low temperature heat pump system with fan-coils. All simulations were performed in dynamic state by using TRNSYS software. The results of the analysis assessing the energy and economic performances of the two systems highlighting the advantages of the BTES application in the context of Italian market.

School buildings represent an energy-consuming sector of real estate where different efficiency actions are necessary. The literature shows how the design of a multi-energy system offers numerous advantages, however, there are problems related to the integration of cogeneration units with renewable energy sources due to the low flexibility of the first one and the high degree of uncertainty of the latter. The authors provide an alternative solution through the analysis of a case study consisting of a multiple energy system in three Sicilian schools, focusing on the system&rsquo, s operational strategy, on the design and sizing of components and trying to exploit the energy needs complementarity of buildings instead of integrating the conventional energy storage systems. Not considering school activities in summer, it was decided to install a cogeneration unit sized on winter thermal loads, whereas the electricity demand not covered was reduced with photovoltaic systems designed to maximize production for seasonal use and with loads concentrated in the morning hours. The effectiveness of this idea, which can be replicated for similar users and areas, is proved by a payback time of less than 11 years and a reduction of 31.77% of the CO2 emissions.

In the future, renewable energy sources will increasingly represent an efficient energy source capable of meeting the demands of residential and industrial buildings avoiding the emissions of greenhouse gases into the atmosphere. In this paper, a heat and electric power cogeneration plant implementing a field of dish-Stirling collectors, a seasonal geothermal storage and a system of water-to-water heat pumps is proposed for the first time. The cogeneration plant has been designed both to supply thermal energy to the heating system of Building 9 of the Department of Engineering in Palermo and to produce electricity. The operation of the plant has been tested by means of hourly-based numerical simulations that have been carried out using a numerical model implemented with Transient System Simulation Tool. The experimental data of a pilot dish-Stirling collector, located in the same area, has been used to carefully calibrate the numerical model. Using energy and economic performance indicators, it was possible to select the best configurations among 1440 analysed cases. Results of simulations show that with the best plant configuration, it is possible to cover 97% of the building's annual thermal loads with energy produced by the solar system. The remaining 64% of electrical energy produced by the electric engines is free to be used for other applications. Financial analyses have shown that market penetration of this type of plant would need a strong support through incentives.

Nowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of statistical quality indexes does not allow an adequate identification of the most efficient method, as the necessary efforts for implementation of the methods from the initial data collection to the use phase are not considered. In this work, the authors propose to apply, for the first time, the multicriteria assessment to determine the most efficient alternative method, used for forecasting of building thermal energy demand. Three alternative “black-box” methods, previously investigated by the authors, were compared by the multiple criteria Complex Proportional Assessment Method. Such a procedure revealed ranking of the methods in four phases, namely Pre-processing, Implementation, Post-processing and Use, as well as overall assessment and selection of the most efficient method in terms of evaluated criteria. This first application could represent an incentive for future multi-criteria analyses involving a growing number of alternative forecasting methods.

The concept of implementing energy savings to reduce greenhouse gas emissions has become a key element of energy policies of any industrialized country. In the civil sector and specifically, energy savings for office buildings, there are still opportunities for further enhancements related to correctly determining the air conditioning thermal requirements. However, there is a lack of simple correlations that allow a preliminary assessment for a single building or correlations that can be quickly applied at the district level. This paper proposes several simple correlations that determine the heating loads of a typical office building by only knowing the Degree-Days of a specific European location. The authors have developed a dynamic model of an office building, considering the different energy regulations in force in several European countries such that the building model is as energy-efficient as possible in each examined location. Furthermore, the standard requirements related to the employment rate, indoor ventilation and indoor gain have been included. The results from several simulations performed in the TRNSYS environment have enabled the development of mathematical relationships valid for seven European countries and three continental zones (northern, central and southern) with notably high correlation coefficients. The proposed equations can be useful for determining the heating load of non-residential buildings with an appropriate level of detail for a rough energy plan at the district level.