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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

The purpose of this paper is to illustrate a procedure that permits, starting from a sufficiently long database of time series, the construction of a Reference Year (RY) of hourly weather data according to the rules of ISO 15927-4 standard. In order to facilitate the management of the weather database and to allow the users to easily generate the file, an algorithm has been implemented in Microsoft Visual Basic for Application (VBA). By this way, the application of the ISO 15927-4 is possible even using a popular software tool such as Microsoft Excel or Access without any other expensive specialized software. Such tool allows to fulfil all the procedures mentioned in ISO 15927- 4 giving as result a time series of 8760 values of several weather variables, ready to be used in any software for energy simulation of buildings.

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.

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.

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 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).

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.

Existing street lighting systems, in most of South Italy cities, are often inefficient due to the obsolescence of lamps and luminaires and of ineffective light control systems unable to implement efficient on-off and dimming strategies. Energy efficiency improvement, in street lighting systems, is often one of the key actions to be adopted by Public Administration in their Sustainable Energy Action Plan in the framework of the “Covenant of Majors” activities. As a task of FACTOR 20 project, a set of planning options has been analysed and proposed. Particularly, street lighting efficiency projects have been studied for representative case studies. A detailed survey of the public lighting systems, in Comiso, allowed represent current performance figures such us installed power, luminance and illuminance levels in roads categories, electricity consumption, switching and dimming schedules. A project of system upgrade has been elaborated. To do this, many lighting simulations, energy and economic assessments in three scenarios have been performed. The obtained results show that high improvements of the lighting quality are foreseeable together with large energy and economic saving. An economic sensitivity analysis, has shown how the performance can change. The proposed methodology can be applied in many similar South Italy cities.

Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, there is no simple and reliable tool that simultaneously solves the energy and environmental balance of buildings. In this work, the authors address this challenge by proposing the application of an Artificial Neural Network. Due to the high reliability of learning algorithms in the resolution of complex and non-linear problems, it was possible to simultaneously solve two different but strongly dependent aspects after a deep training phase. In previous researches, the authors applied several topologies of neural networks, which were trained on a large and representative database and developed for the Italian building stock. The database, characterised by several building models simulated in different climatic conditions, collects 29 inputs (13 energy data and 16 environmental data) and provides 7 outputs, 1 for heating energy demand and 6 of the most used indicators in life cycle assessment of buildings. A statistical analysis of the results confirmed that the proposed method is appropriate to achieve the goal of the study. The best artificial neural network for each output presented low Root Mean Square Error, Mean Absolute Error lower than 5%, and determination coefficient close to 1. The excellent results confirmed that this methodology can be extended in any context and to any condition (other countries and building stocks). Furthermore, the implementation of this solution algorithm in a software program can enable the development of a suitable decision support tool, which is simple, reliable, and easy to use even for a non-expert user. The possibility to use an instrument to predict a building's performance in its design and planning phase, represent an important result to support decision-making processes toward more sustainable choices.

In this work the realization of an energetic hub based on a high-performances dish Stirling system, at the DEIM of the University of Palermo, is presented. The realized system is the first top rated solar thermodynamic plant directly connected to the national electric grid. The connection permits the electricity injection into the grid and allows to access to national renewable energy incentives regime. The system realization was possible thanks to an international cooperation between the University of Palermo and the private company Horizon S.r.l., official partner of Ripasso Energy AB, owner of this technology. Initial data of the plant energy production and the foreseen improvements for the hybridization of the system and its integration with an electrical and geothermal storage system are also presented.

The exploitation of renewable energy sources and specifically photovoltaic (PV) devices have been showing significant growth; however, for a more effective development of this technology it is essential to have higher energy conversion performances. PV producers often declare a higher efficiency respect to real conditions and this deviation is mainly due to the difference between nominal and real temperature conditions of the PV. In order to improve the solar cell energy conversion efficiency many authors have proposed a methodology to keep the temperature of a PV system lower: a modified crystalline PV system built with a normal PV panel coupled with a Phase Change Material (PCM) heat storage device. In this paper a thermal model analysis of the crystalline PV-PCM system based on a theoretical study using finite difference approach is described. The authors developed an algorithm based on an explicit finite difference formulation of energy balance of the crystalline PV-PCM system. Two sets of recursive equations were developed for two types of spatial domains: a boundary domain and an internal domain. The reliability of the developed model is tested by a comparison with data coming from a test facility. The results of numerical simulations are in good agreement with experimental data.

Exploiting the equivalent one-diode circuit of a photovoltaic (PV) module, this paper proposes a novel and fully analytical model to predict the electrical performance upon solar irradiance intensity and PV module temperature. The model refers essentially to an equivalent circuit governed by five parameters and the extraction of them permits to describe the current–voltage curve of the PV panel and consequently permits to assess the energy output of PV modules. The proposed model extracts the five characteristic parameters using only exact analytical relationship and tabular data always available such as short-circuit current, open circuit voltage and the Maximum Power Point (MPP). The difference with other models consists in the complete absence of mathematical simplifications or other physical assumptions. All used equations were obtained with a transparent analytical procedure. A new resolution procedure for solving the equation that describes the equivalent one diode circuit system is also described. The procedure is based upon the Generalized Reduced Gradient (GRG) algorithm and transforms the extraction of the five parameters into a constrained non-linear optimization problem. The purely analytical model, the absence of data to be obtained from graphic methods or not always available in data-sheets, and a new optimized procedure to solve the system of equations lead to obtain values of the five parameters that perfectly fit the official tabular data. The suggested procedure of numerical solution of a local minimum problem allows converging towards the solution with the desired accuracy in a fast and effective way. Although in the scientific literature there are several models able to determine the value of these five parameters, these procedures are always affected by inevitable inaccuracies linked to various simplifications or due to the use of non-tabular data such as some graphic characteristics of the experimental I–V curve (moreover not always available). The model, as opposed to those already known in the literature, is exclusively based on analytical relationships and is free of any simplifications that may affect the reliability of the results. The proposed model allows a more accurate modeling of the PV modules based solely on reference data and the availability of decision support tools that may reliably predict the energy produced by a photovoltaic panel is essential in the design phase of the plant to avoid future problems related to incorrect sizing. Furthermore, reliable energy predictions lead to more correct economic analyses that can stimulate the diffusion of the PV technology.

The paper presents a study on the opportunities offered by ICT for improving the efficiency of the generation and distribution system of a small island. The island taken into consideration is the Italian Lampedusa island. In the paper, the power distribution system of the island is described and the summer and winter daily load profiles are examined in order to define the more suitable control actions for improving the generation and distribution efficiency of the power system. Finally the architecture of an idoneous control system for the smart grid is proposed and an evaluation of the purchase and installation costs of components and devices for the transition of a traditional house towards a smart house is presented. © 2016 IEEE.

Buildings energy consumption depends on several parameters, such as climate, envelope typologies, occupant behavior, intended use, etc.; assessment of the energy performance of a building requires substantial input data describing constructions, environmental contexts, thermo-physical properties, geometry, control strategies and several other parameters influencing the thermal balance. In the last years, several numerical approaches dedicated to building simulation have been tested developing specialized software. On the other hand, simplified building models permit the evaluation of indoor conditions and heating/cooling loads with a good level of accuracy and without excessive computational costs or user expertize. The authors tried to establish a set of simple correlations to permit a fast preliminary assessment of heating energy demand for office buildings. Data employed to build the correlations come from detailed dynamic simulations performed in TRNSYS 17 environment; these models were build according to the legal limits of Italian standards and laws concerning low energy requirements. The authors identified specific locations for different Italian climatic zones and simulated three models with different shape factors (S/V = 0.24, 0.5 and 0.9). The obtained results allowed to determine simple and direct correlations among heating energy demand, Heating Degree Days and S/V values.

The performances of a photovoltaic panel (PV) are defined according to the “peak power” that identifies the maximum electric power supplied by the panel when it receives an insolation of 1 kW/m2 and the temperature of the cell is maintained at 25 °C. These conditions are only nominal since the solar radiation has a variable intensity and also the panel is subjected to thermal excursions; due to these reasons the real power efficiency of the panel is considerably lower than that obtainable in the nominal conditions. The study focused on assessing a method to reduce the peak temperatures of PV systems using Phase Change Materials (PCM). To this aim it was created a numerical model capable to describe the behaviour of a PV-PCM systems. The results were validated and compared with real data obtained from a test facility.

This paper presents a new mathematical approach, which, when applied to conduction transfer functions (CTFs) of a multi-layered wall, is able to predict the reliability of building simulations. This new procedure can be used to identify the best set of CTF coefficients, which are a critical point at the core of the ASHRAE calculation methodology founded on the transfer function method. To evaluate the performance of different CTF coefficient sets, the authors performed a large number of thermal simulations on the multi-layered walls included in the ASHRAE Handbook, volume fundamentals, and on other walls typical of Mediterranean building heritage. Those data were employed to test an algorithm able to assess the reliability of the simulations. The numerical results show that it is possible to select the optimal number of coefficients on the basis of the size of the poles of a CTF. The proposed criterion, which employs a pole threshold value, is highly accurate, fast and easy to integrate in the most diffuse building simulation tools.

Degree-days (DD) are a climatic indicator that can be used in the assessment and analysis of weather related to energy consumption of buildings. Essentially, degree-days are a summation of the differences between the outdoor temperature and some reference (or base) temperature over a specific time period. In literature, different method can be used for determining the DD value and generally the choice depends on the availability of climatic data of each location. In this paper, after a review and comparison of the most common approaches used to determine DD, the Italian procedure was deeply analyzed. The application of Italian technical rules is based on weather data calculated on a monthly time series monitored before 1994. The obsolescence of the used weather data leads to an incorrect assessment of energy performances. Taking into account the climatic change that in the last years has affected Italy land, the aim of the paper is to assess the impact of new DD values in calculating energy demand of buildings. For these reasons, in this paper the authors recalculated DD of some Italian cities, considering the average monthly temperatures of the last decade. Data were extracted from Meteonorm 7, one of the most popular software for the statistical processing of climate data. Furthermore, other datasets were generated considering future scenarios defined by IPCC (Intergovernmental Panel on Climate Change). A comparison with the official DD issued by current legislation and new DD recalculated with more recent data highlighted how climate change have affected the calculation of this parameter

In order to achieve the ambitious objective of decarbonising the economy, it is mandatory, especially in Europe and in Italy, to include the retrofitting of existing buildings. In a country where a large share of existing buildings have heritage value, it is important to design effective retrofit solutions also in historical buildings. In this context, the paper describes the 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, and its energy uses were thoroughly investigated. A building model was developed in the TRNSYS environment and its performances validated. The validated model was used for redesign studies aimed towards the achievement of the Net Zero Energy Building target. The best performing solutions to be applied to a case study like the Sanfilippo House were those regarding the management of the building, as in the case of the natural ventilation and the energy systems 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 they should be disregarded.

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

Determining the operating temperatureTcof photovoltaic panelsPVis important in evaluating the actual performance of these systems. In the literature, different correlations exist, in either explicit or implicit forms, which often do not account for the electrical behaviour of panels; in this way, estimatingTcis based only on the passive behaviour of thePV. In this paper, the authors propose a new implicit correlation that takes into account the standard weather variables and the electricity production regimes of aPVpanel in terms of the proximity to the maximum power points. To validate its reliability, the new correlation was tested on two different PV panels (Sanyo and Kyocera panels) and the results were compared with values obtained from other common correlations already available in the literature. The data show that the quality of the new correlation drastically improves the estimation of the photovoltaic operating temperature.

Existing street lighting systems in most of Sicilian cities are often inappropriate due to the obsolescence of light sources and luminaires and of unsuitable light control systems unable to provide efficient on-off and dimming strategies. Improvement of energy efficiency in street lighting systems can be one of the key actions adopted by Public Administration in developing Sustainable Energy Action Plan in the framework of the “Covenant of Majors” activities. As a task of FACTOR 20 project a set of planning options have been analysed and proposed for Sicilian cities. Particularly, street lighting efficiency projects have been studied for representative case studies. A detailed survey of the public lighting systems in the city of Comiso (RG) allowed to know and represents current and design performance figures such us installed power, luminance and illuminance levels in roads categories, electricity consumption, switching and dimming schedules. To do this many lighting simulations have been performed. Several scenarios have been proposed. The results obtained show that high improvements of the lighting quality are foreseeable together with large energy and economic saving. The proposed methodological approach can be applied in most of Sicilian and South Italy cities.

The LIFE+ Programme is the European Union’s funding instrument for the environment. The general objective of LIFE+ is to contribute to the implementation, updating and development of EU environmental policy and legislation by co-financing pilot or demonstration projects with European added value. In the framework of LIFE+, the project “Forwarding demonstrative ACTions On a Regional and local scale (FACTOR20) to reach EU targets of the European Plan 20/20/20” was founded by European Commission. FACTOR20 is aimed to define a set of tools to support the planning of regional and national policies for the reduction of greenhouse gas emissions and for the reduction of energy consumption. The knowledge of the existing building allow to quantify energy consumption of an urban area and to highlight what are the main energy problems on which to intervene. One of these tools is the definition of a new building regulation schema that identifies the best practices to improve the energy efficiency, to reduce the GHG emissions and to promote the use of RES. The authors, in order to assess the applicability and the effectiveness of some key actions proposed in the new building regulation plan schema, have performed a detailed dynamic analysis of energy consumptions related to typical building structures strongly representative of Sicilian context. The simulations, carried out by using TRNSYS17, have permitted to assess the actual energy consumptions and then to compare the new energy performances induced by the application of some key retrofit actions. In this way it was possible to identify which retrofit action is more convenient from the point of view of energy and environmental; also the designer have an indication to the designer on the priorities of retrofit actions.

The use of novel building materials that contain active thermal components would be a major advancement in achieving significant heating and cooling energy savings. In the last 40 years, Phase Change Materials or PCMs have been tested as thermal mass components in buildings, and most studies have found that PCMs enhance the building energy performance. The use of PCMs as an energy storage device is due to their relatively high fusion latent heat; during the melting and/or solidification phase, a PCM is capable of storing or releasing a large amount of energy. PCMs in a wall layer store solar energy during the warmer hours of the day and release it during the night, thereby decreasing and shifting forward in time the peak wall temperature. In this paper, an algorithm is presented based on the general Fourier differential equations that solve the heat transfer problem in multi-layer wall structures, such as sandwich panels, that includes a layer that can change phase. In detail, the equations are proposed and transformed into formulas useful in the FDM approach (finite difference method), which solves the system simultaneously for the temperature at each node. The equation set proposed is accurate, fast and easy to integrate into most building simulation tools in any programming language. The numerical solution was validated using a comparison with the Voller and Cross analytical test problem.

In this paper, we describe a method for classifying the workplaces as regards the noise risk. This method provides an univocal classification of work area and allows to correctly implement the control measures in accordance with a suitable priority scale. Finally, this method can be easily adjusted to respect various national standards.

The need to reduce energy consumptions and to optimize the processes of energy production has pushed the technology towards the implementation of hybrid systems for combined production of electric and thermal energy. In particular, recent researches look with interest at the installation of hybrid system PV/T. To improve the energy performance of these systems, it is necessary to know the operating temperature of the photovoltaic modules. Furthermore, when photovoltaic (PV) systems replace the traditional building envelope materials and they are fully integrated (building integrated photovoltaic (BIPV)), it is very important to correctly assess their thermal behaviour. The determination of the operating temperature T c is a key parameter for the assessment of the actual performance of photovoltaic panels. In the literature, it is possible to find different correlations that evaluate the T c referring to standard test conditions and/or applying some theoretical simplifications/assumptions. Nevertheless, the application of these different correlations, for the same conditions, does not lead to unequivocal results. In this work, an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. This methodology does not require any simplifications nor physical assumptions: on the contrary, the ANN is a black box that learn from actual data, allowing to obtain good results. In the paper is described the ANN that obtained the best performances: a multilayer perception network. The results have been compared with experimental monitored data and with some of the most cited empirical correlations proposed by different authors.

The use of reliable forecasting models for the PV temperature is necessary for a more correct evaluation of energy and economic performances. Climatic conditions certainly have a remarkable influence on thermo-electric behaviour of the PV panel but the physical system is too complex for an analytical representation. A neural-network-based approach for solar panel temperature modelling is here presented. The models were trained using a set of data collected from a test facility. Simulation results of the trained neural networks are presented and compared with those obtained with an empirical correlation.

The operating temperature of a commercial PV panel strongly affects the electric power output of the devices. The prediction of the operating temperature therefore plays a central role in the economic and energy assessment of PV systems. The authors propose a new implicit correlation that takes into account the standard weather variables but also the regimen of PV panel in terms of the proximity to the maximum power points. Data coming from an experimental setup were processed by using the least squares technique. To validate the reliability of the correlation, a comparison between expected values and data from other common correlations were done. The results show that the regimen of the panel is strongly correlated with the operating temperature and the quality of the new correlation is generally very good.

U-value of glazed elements is often a critical issue because these components, due to their small thickness and to the poor resistance of the glass and frame materials, cause very relevant heat fluxes. This paper presents an investigation on the thermal properties of a particular glazed component: the glassblock. Generally standard glassblocks have high U- values in comparison to the maximum values allowed by energy efficiency standards for glazed surfaces. This paper reports a summary of possible solutions that could improve the performances of the glassblock. A set of new configurations of the glassblock has been defined by schematic models and their overall thermal resistance has been assessed by the means of Finite Element software. The resulting performances are presented in terms of the global thermal transmittance of the modified glassblocks, also considering the effects of sealing and mortar. The paper also shows some significant potential improvements to address new production lines.

La diffusione commerciale dei dispositivi fotovoltaici presenta negli ultimi anni un trend di crescita significativa a livello internazionale. Numerose ricerche sono state svolte al fine di incrementare l’efficienza di conversione elettrica dei pannelli fotovoltaici e tra le soluzioni indagate è stato sperimentato l’impiego di materiali a cambiamento di fase (MCF) per ridurre i picchi di temperatura di funzionamento. Nel lavoro è stato sviluppato un algoritmo di calcolo in grado di fornire l’andamento temporale della temperatura di un pannello fotovoltaico accoppiato a materiale a cambiamento di fase (Sistema PV-MCF). La determinazione della distribuzione di temperature che interessano un sistema PV-MCF è stata affrontata con il metodo delle differenze finite in geometria monodimensionale. Sono state definite le equazioni di conservazione dell’energia per ogni punto nodale in cui è stato discretizzato il sistema fisico, differenziandole per i nodi “interni” e per i nodi “superficiali”. Le equazioni alle differenze finite sono state formulate esplicitamente rispetto alla variabile temporale e sono state codificate in un apposito software. Il confronto con alcune soluzioni analitiche largamente impiegate nella bibliografia di settore ha permesso di verificare la correttezza e la validità dell’approccio sviluppato.

Allo scopo di valutare la possibilità di sfruttare l’energia eolica anche in zone fortemente urbanizzate recentemente la Comunità Europea ha posto tra le sue priorità scientifiche la redazione di atlanti eolici a piccola scala. Al fine di fornire dati che possano condurre ad una corretta valutazione eolica di centri urbani, nel seguente lavoro vengono analizzate le caratteristiche del regime anemometrico dell'area urbana di Palermo, registrate da un rete di monitoraggio automatica afferente al Dipartimento di Ricerche Energetiche ed Ambientali (DREAM) e composta da quattro stazioni meteo. Dopo un esame statistico preliminare, in cui le serie temporali della velocità del vento sono state divise e analizzate per andamenti stagionali, sono state impiegate le distribuzioni di densità di probabilità (PDF) Weibull e Gamma per descrivere la distribuzione frequenziale della velocità del vento rilevata. Per ogni centralina e per ogni periodo temporale sono state calcolate le distribuzioni, i parametri di scala e di forma e la loro stima è stata effettuata utilizzando gli stimatori di massima verosimiglianza. La bontà di adattamento ai dati reali delle funzioni di probabilità così desunte è stata valutata da due classici test statistici: Kolmogorov-Smirnov e Anderson-Darling. Infatti una corretta valutazione dei fattori di scala e di forma può consentire una accurata analisi energetica ed economica della risorsa microeolica.

A new five-parameters model to describe the relation between the electric current and the voltage for a photovoltaic module is experimentally validated on the field, with variable conditions of operative temperature and solar irradiance. The electrical parameters of the one diode equivalent circuit are found by solving an equations system based on the data commonly issued by manufacturers in standard test conditions. To verify the capability of the new model to fit PV panel characteristics, the model was tested on two different panels comparing the results both with the data issued by manufacturers and with the results obtained using the five- parameters model already proposed by other Authors. The comparison shows that the new model is able to reproduce with very good precision the I-V curve issued by manufactures. Furthermore, the reliability of the proposed model was assessed performing an experimental validation connecting a PV panel to several different electrical resistances. The simultaneous measurement of the silicon temperature, air temperature, wind speed and direction, solar irradiance and voltage drop across the load, has permitted to verify a very good correspondence between the measured and the calculated data.

The use of ligno-cellulosic residuals from agriculture as means for renewable energy production is well known; nonetheless significant problems still exist on development of bioenergy value chains. Moisture content and hygroscopicity are among these problems. Higher moisture content of the biomass means lower calorific value and higher perishability, hence storage difficulties. For this reason it is important to evaluate how the moisture content of the material varies while it is stored and how the calorific value of the feedstock is affected by it. The purpose of this study is to determine the variation of moisture content and its influence on lower calorific value (LCV) of four types of solid residual biomass: oven dried chipped olive tree trimmings, milled olive pomace; oven dried olive tree trimmings and fresh olive tree trimmings. Moisture content of the samples is calculated by means of thermogravimetric analysis (TGA) according to standard CEN/TS 14774-2:2004, while the LCVs are calculated by means of a calorimeter, following standard UNI EN 14918:2010. All the biomass samples were left to reach equilibrium moisture content (EMC) in a temperature and humidity controlled environment. Two different phases were analyzed with respect to moisture uptake rates: 1) fast moisture uptake rate phase (first four hours of exposure) and 2) slow moisture uptake rate, (the days following the first four hours of exposure). Samples experiencing fast moisture uptake rate, during the first four hours of exposure, were kept in a monitored ambient at T=22 ± 1oC and RH=59 ± 2%, while samples exposed to slow moisture uptake rate were kept in a climatic chamber at three different set of temperature and humidity controlled environment simulating the climatic conditions in different periods of the year in Enna province: 10oC - 80% RH (winter), 15oC - 70% RH (spring/autumn) and 20oC - 55% RH, (summer). The results obtained show that the olive pruning chips (0.425 mm to 1.00 mm and 1.00 mm to 2.00 mm particle size ranges) stored in a heap in a controlled climatic chamber require approximately 20 days to reach EMC. Depending on the particle size range EMCs reach the values of 6.2 and 7.5% by weight in the "summer" condition, 14.3% and 16.9% in "spring/autumn" condition, 24.1% and 28.2% in "winter" condition. Moisture is absorbed gradually over time and results show that in the first four hours, the dry sample exposed to ambient condition (T=22 ± 1 oC and RH=59 ± 2%) reaches a moisture content between 0.75 and 0.96% of its weight; the LCV at equilibrium is evaluated between 18, 576 J•odg-1 and 18, 793 J•odg-1, the higher value related to the bigger particle size range examined. Pomace heap under the same experimental set up and time period, reaches an equilibrium moisture content of 8.5% and 9.7% (summer conditions), 19.2% and 22.0% (spring/autumn condition) and between 30.9% and 34.1% (winter condition). Olive pomace accumulates moisture relatively faster than olive trimmings as dry sample recovers about 1.61% to 1.97% of moisture in the first four hours of exposure. The LCV at equilibrium is determined between 20, 145 J•odg-1 and 20, 436 J•odg-1. Pruning dried samples reach an EMC equal to 6.1% in approximately 25 days, with a LCV equal to18, 921 J•odg-1 ± 1.9%. Heaps of fresh prunings reach an EMC of 8.2% in approximately 20 days; LCV of the feedstock is 19, 356 J•odg-1± 1.6%.©2006-2014 Asian Research Publishing Network (ARPN). All rights reserved.