1,721,063 research outputs found
Numerical analysis of a concentrating solar power plant integrating solid thermal storage systems for biofuel production
No full textReplacing hydrocarbons with biofuels will be a key step in the global decarbonisation process. Among the various biomasses, microalgae represent a particularly sustainable biological resource that can be used for the production of biofuels. In this study, a new concentrating solar power plant scheme that can be integrated with chemical reactors in which the hydrothermal processes for liquefaction of microalgae take place is proposed. The energy efficiency of the proposed plant scheme, which contemplates the use of concrete sensible heat storage systems, was analysed by means of hourly numerical simulations conducted using a specially developed TRNSYS model
Energy and Environmental Assessment of a Hybrid Dish-Stirling Concentrating Solar Power Plant
Full text linkAlthough the 2019 global pandemic slowed the growing trend of CO2 concentrations in the atmosphere, it has since resumed its rise, prompting world leaders to accelerate the generation of electricity from renewable sources. The study presented in this paper is focused on the evaluation of the energy and environmental benefits corresponding to the hypothesis of hybridizing a dish-Stirling plant installed on the university campus of Palermo (Italy). These analyses were carried out by means of dynamic simulations based on an accurate energy model validated with the experimental data collected during the measurement campaign that occurred during the period of operation of the reference plant. Assuming different scenarios for managing the production period and different fuels, including renewable fuels, it was found that the annual electricity production of the dish-Stirling system operating in solar mode can be increased by between 47% and 78% when hybridized. This would correspond to an increase in generation efficiency ranging from 4% to 16%. Finally, assuming that the dish-Stirling system is hybridized with renewable combustible gases, this would result in avoided CO2 emissions of between approximately 1594 and 3953 tons over the 25-year lifetime of the examined plant
Calculation of Energy Performance Indices of Daylight Linked Control Systems by Monitored Data
Full text linkThe actual performances of Building Automation systems are often lower than the ideal ones. In order to
investigate the actual performance of a Building Automation system for lighting control, a large stock of
collected data, including indoor illuminance and absorbed electric power, have been presented and analysed
in this paper. The measures have been taken during one year, in a laboratory located at the University of
Palermo, where different lighting control systems, produced by two different manufacture companies, have
been installed. As demonstrated in literature, many factors affecting energy savings’ evaluation in lighting
control systems are the position and the typology of the sensors and their configuration. Furthermore, using
the collected data, a set of indices has been calculated. It is able to test the performance of the systems in terms
of energy efficiency and fulfilment of visual comfort tasks, according to different natural light availability,
lighting system configurations and time scenarios. Finally, the performances of the two above lighting control
systems have been compare
Development of Neural Network Prediction Models for the Energy Producibility of a Parabolic Dish: A Comparison with the Analytical Approach
Full text linkSolar energy is one of the most widely exploited renewable/sustainable resources for electricity generation, with photovoltaic and concentrating solar power technologies at the forefront of research. This study focuses on the development of a neural network prediction model aimed at assessing the energy producibility of dish–Stirling systems, testing the methodology and offering a useful tool to support the design and sizing phases of the system at different installation sites. Employing the open-source platform TensorFlow, two different classes of feedforward neural networks were developed and validated (multilayer perceptron and radial basis function). The absolute novelty of this approach is the use of real data for the training phase and not predictions coming from another analytical/numerical model. Several neural networks were investigated by varying the level of depth, the number of neurons, and the computing resources involved for two different sets of input variables. The best of all the tested neural networks resulted in a coefficient of determination of 0.98 by comparing the predicted electrical output power values with those measured experimentally. The results confirmed the high reliability of the neural models, and the use of only open-source IT tools guarantees maximum transparency and replicability of the models
Toward a Sustainable Indoor Environment: Coupling Geothermal Cooling with Water Recovery Through EAHX Systems
Full text linkThis study presents a preliminary analysis of an innovative system that combines indoor air conditioning with water recovery and storage. The device integrates Peltier cells with a horizontal Earth-to-Air Heat Exchanger (EAHX), exploiting the ground stable temperature to enhance cooling and promote condensation. Warm, humid air is pre-cooled via the geothermal pipe, then split by a fan into two streams: one passes over the cold side of the Peltier cells for cooling and dehumidification, while the other flows over the hot side and heats up. The two airstreams are then mixed in a water storage tank, which also serves as a thermal mixing chamber to regulate the final air temperature. The analysis investigates the influence of soil thermal conditions on condensation within the horizontal pipe and the resulting cooling effect in indoor spaces. A hybrid simulation approach was adopted, coupling a 3D model implemented in COMSOL Multiphysics® with a 1D analytical model. Boundary conditions and meteorological data were based on the Typical Meteorological Year (TMY) for Palermo. Two scenarios were considered. In Case A, during the hours when air conditioning is not operating (between 11 p.m. and 9 a.m.), air is circulated in the exchanger to pre-cool the ground and the air leaving the exchanger is rejected into the environment. In Case B, the no air is not circulated in the heat exchanger during non-conditioning periods. Results from the June–August period show that the EAHXs reduced the average outdoor air temperature from 27.81 °C to 25.45 °C, with relative humidity rising from 58.2% to 66.66%, while maintaining nearly constant specific humidity. The system exchanged average powers of 102 W (Case A) and 96 W (Case B), corresponding to energy removals of 225 kWh and 212 kWh, respectively. Case A, which included nighttime soil pre-cooling, showed a 6% increase in efficiency. Condensation water production values range from around 0.005 g/s with one Peltier cell to almost 0.5 g/s with seven Peltier cells. As the number of Peltier cells increases, the cooling effect becomes more pronounced, reducing the output temperature considerably. This solution is scalable and well-suited for implementation in developing countries, where it can be efficiently powered by stand-alone photovoltaic systems
ALTERNATIVE MODELS FOR BUILDING ENERGY PERFORMANCE ASSESSMENT
Full text linkThe research activity carried out during the three years of the PhD course attended, at the Engineering Department of the University of Palermo, was aimed at the identification of an alternative predictive model able to solve the traditional building thermal balance in a simple but reliable way, speeding up any first phase of energy planning. Nowadays, worldwide directives aimed at reducing energy consumptions and environmental impacts have focused the attention of the scientific community on improving energy efficiency in the building sector. The reduction of energy consumption and CO2 emissions for heating and cooling needs of buildings is an important challenge for the European Union, because the buildings sector contributes up to 36% of the global CO2 emissions [1] and up to 40% of total primary energy consumptions [2].
Despite the ambitious goals set by the Energy Performance of Buildings Directive (EPBD) at the European level [1], which states that, by 2020, all new buildings and existing buildings undergoing major refurbishments will have to be Nearly Zero Energy Buildings (NZEB) [3,4], the critical challenge remains the improvement of the efficiency when upgrading the existing building stock to standards of the NZEB level [5]. The improvement of the energy efficiency of buildings and their operational energy usage should be estimated early in the design phase to guarantee a reduction in energy consumption, so buildings can be as sustainable as possible [6]. While a newly constructed NZEB can employ the “state of the art” of available efficient technologies and design practices, the optimization of existing buildings requires better efforts [7]. One way or the other, the identification of the best energy retrofit actions or the choice of a better technological solution to plan a building is not so simple. It has become one of the main objectives of several research studies, which require deep knowledge in the field of the building energy balance.
The building thermal balance includes all sources and sinks of energy, as well as all energy that flows through its envelope. More in detail, the energy demand in buildings depends on the combination of several parameters, such as climate, envelope features, occupant behaviour and intended use. Indeed, the assessment of building energy performance requires substantial input data describing structures, environmental conditions [8], thermo-physical properties of the envelope, geometry, control strategies, and several other parameters. From the first design phases designers and researchers, which are trying to respect the prescriptions of the EPBD directive and to simultaneously ensure the thermal comfort of the occupants, must optimize all possible aspects that represent the key points in the building energy balance.
As will be shown in Chapter A, the literature offers highly numerous complex and simplified resolution approaches [9]. Some are based on knowledge of the building thermal balance and on the resolution of physical equations; others are based on cumulated building data and on implementations of forecast models developed by machine-learning techniques [10].
Several numerical approaches are most widespread; these have undergone testing and implementing in specialised software tools such as DOE-2 [11], Energy Plus [12], TRNSYS [13] and ESP-r [14]. Such building modelling software can be employed in several ways on different scales; they can be simplified [15,16] or detailed comprehensively by different methods and numerical approaches [17]. Nevertheless, they are often characterised by a lack of a common language, which constitutes an obstacle for making a suitable choice. It is often more convenient to accelerate the building thermal needs evaluation and use the simplified methods and models. For example, a steady state approach for the evaluation of thermal loads is characterised by a good level of accuracy and low computational costs. However, its main limitation is that some phenomenon, such as the thermal inertia of the building envelope/structure, may be completely neglected.
On the other hand, the choice of a more complex solution, such as the dynamic approach, uses very elaborate physical functions to evaluate the energy consumption of buildings. Although these dynamic simulation tools are effective and accurate, they have some practical difficulties such as collecting detailed building data and/or evaluating the proper boundary conditions. The use of these tools normally requires an expert user and a careful calibration of the model and do not provide a generalised response for a group of buildings with the same simulation, because they support a specific answer to a specific problem. Meanwhile the lack of precise input can lead to low-accuracy simulation. Anyway, in all cases it is necessary to be an expert user to implement, solve and evaluate the results, and these phases are not fast and not always immediately provide the correct evaluation, conducting the user to restart the entire procedure.
In the field of energy planning, in order to identify energy efficiency actions aimed at a particular context, could be more convenient to speed up the preliminary assessment phase resorting to a simplified model that allows the evaluation of thermal energy demand with a good level of accuracy and without excessive computational cost or user expertise.
The aim of this research, conducted during the three years of the PhD studies, is based on the idea of overcoming the limits previously indicated developing a reliable and a simple building energy tool or an evaluation model capable of helping an unskilled user at least in the first evaluation phase.
To achieve this purpose, the first part of the research was characterised of an in-depth study of the sector bibliography with the analysis of the most widespread and used methods aimed at solving the thermal balance of buildings. After a brief distinction of the analysed methods in White, Black and Grey Box category, it was possible to highlight the strengths and weaknesses of each one [9].
Based on the analysis of this study, some alternative methods have been investigated. In detail, the idea was to investigate several Black-Box approaches; mainly used to deduce prediction models from a relevant database. This category does not require any information about physical phenomena but are based on a function deduced only by means of sample data connected to each other and which describes the behaviour of a specific system. Therefore, it is fundamental the presence of a suitable and well-set database that characterise the problem, so that the output data are strongly related to one or more input data. The completely absence of this information and the great difficulty in finding data, has led to the creation of a basic energy database which, under certain hypotheses, is representative of a specific building stock.
For this reason, in the first step of this research was developed a generic building energy database that in a reliable way, and underlining the main features of the thermal balance, issues information about the energy performances.
In detail, two energy building databases representative of a non-residential building-stock located in the European and Italian territory have been created. Starting from a well-known and calibrated Base-Case dynamic model, which simulates the actual behaviour of a non-residential building located in Palermo, it was created an Ideal Building representative of a new non-residential building designed with high energy performances in accordance whit the highest standard requirements of the European Community. Taking into consideration the differences existing in the regulations and technical standards about the building energy performance of various European countries, several detailed dynamic simulation models were developed. Moreover, to consider different climatic characteristics, different locations were evaluated for each country or thermal zone which represent the hottest, the coolest and the mildest climate. The shape factor of buildings, which represents the ratio between the total of the loss surfaces to the gross heated volume of a building, was varied from 0.24 to 0.90.
To develop a representative database where the data that identify the building conditions are the inputs of the model linked to an output that describes the energy performances it was decided to develop a parametric simulation. In detail different transmittance values, boundary conditions, construction materials, and energy carriers were chosen and employed to model representative building stocks of European and Italian cities for different climatic zones, weather conditions, and shape factor; all details and the main features are described in Chapter B.
These two databases were used to investigated three alternative methods to solve the building thermal balance; these are:
• Multi Linear Regression (MLR): identification of some simple correlations that uses well known parameters in every energy diagnosis [18–20];
• Buckingham Method (BM): definition of dimensionless numbers that synthetically describe the relationships between the main characteristic parameters of the thermal balance [21]; and
• Artificial Neural Network (ANN): Application of a specific Artificial Intelligence (AI) to determine the thermal needs of a [22] building.
These methods, belonging to the Black-Box category, permit solving a complex problem easier with respect to the White-Box methods because they do not require any information about physical phenomena and expert user skills. Only a small amount of data on well-known parameters that represent the thermal balance of a building is required.
The first analysed alternative method was the MLR, described in Chapter C. This approach allowed to develop a simple model that guarantees a quick evaluation of building energy needs [19] and is often used as a predictive tool. It is reliable and, at the same time, easy to use even for a non-expert user since an in-depth knowledge in the use phase is not needed, and computational costs are low. Moreover, the presence of an accurate input analysis guarantees greater speed and simplicity in the data collection phase [23]. The basis for this model is the linear regression among the variables to forecast and two or more explanatory variables. The feasibility and reliability of MLR models is demonstrated by the publication of the main achieved results in international journals. At first, the MLR method was applied on a dataset that considered heating energy consumptions for three configurations of non-residential buildings located in seven European countries. In this way, it was developed a specific equation for each country and three equations that describe each climatic region identified by a cluster analysis; these results were published in [19]. In a second work [18], it was applied the same methodology to a set of data referring to buildings located in the Italian peninsula. In this case, three building analysed configurations, in accordance to Italian legislative requirements regarding the construction of high energy performance buildings, have been employed. The achievement of the generalised results along with a high level of reliability it was achieved by diversifying each individual model according to its climate zone. It was provided an equation for each climate zone along with a unique equation applicable to the entire peninsula, obviously with different degrees of reliability. An improved version of the latest work concerning the Italian case study appeared in the paper published in [20]. The revised model provided an ability to predict the energy needs for both heating and cooling. Furthermore, to simplify the data retrieval phase that is required for the use of the developed MLR tool, an input selection analysis based on the Pearson coefficient has been performed. In this way the explanatory variables, needful for an optimal identification of thermal loads, have been identified. Finally, a comprehensive statistical analysis of errors ensured high reliability.
The second analysed alternative method represents an innovative approach in developing a flexible and efficient tool in the building energy forecast framework. This tool predicts the energy performance of a building based some dimensionless parameters implemented through the application of the Buckingham theorem. A detailed description of the methodology and results is discussed in the Chapter D and is also published in [21]. The Buckingham theorem represents a key theorem of the dimensional analysis since it is able to define the dimensionless parameters representing the building balance [24]. These parameters define the relationships between the descriptive variables and the fundamental dimensions. Such a dimensional analysis guarantees that the relationship between physical quantities remains valid, even if there is a variation of the magnitudes of the base units of measurement [25]. The dimensional analysis represents a good model to simplify a problem by means of the dimensional homogeneity and, therefore, the consequent reduction in the number of variables. Therefore, this model works well with different applications such as forecasting, planning, control, diagnostics and monitoring in different sectors. The application of the BM for predicting the energy performance of buildings determined nine ad hoc dimensionless numbers. The identification of a set of criteria and a critical analysis of the results allowed to immediately determine thought the dimensionless numbers and without using any software tool, the heating energy demand with a reliability of over 90%. Furthermore, the validation of the proposed methodology was carried out by comparing the heating energy demand that was calculated by a detailed and accurate dynamic simulation.
The last Black-Box examined model was the application of Artificial Neural Networks. The ANNs are the most widely used data mining models, characterised by one of the highest levels of accuracy with respect to other methods but generally have higher computational costs in the developing phase [26]. The design of a neural network, inspired by the behaviour of the human brain, involves the large number of suitably connected nodes (neurons) that, upon applications of simple mathematical operations, influence the learning ability of the network itself [27]. Also in this case, as described in Chapter E, this methodology was applied at the two different energy databases. In [22], the ANN was used to predict the demand for thermal energy linked to the winter climatization of non-residential buildings located in European context, while in another work under review, the ANN was used to determine the heating and cooling energy demand of a representative Italian building stock. The validation of the ANNs was carried out by using a set of data corresponding to 15% of the initial set which were not used to train the ANNs. The obtained good results (determination coefficient values higher than 0.95 and Mean Absolute Percentage Error lower than 10%) show the suitability of the calculation model based on the use of adaptive systems for the evaluation of energy performance of buildings.
Simultaneously, a deep analysis of the investigated problem, underlines how to determine the thermal behaviour of a building trough Black-Box models, particular attention must be paid to the choice of an accurate climate database that along with thermophysical characteristics, strongly influence the thermal behaviour of a building [9].
In detail, to develop a predictive model of thermal needs, it is also necessary to pay close attention to the climate aspects. In the literature, many studies use the degree day (DD) to predict building energy demand, but this assessment, through the use of a climatic index, is correct only if its determination is a function of the same weather data used for the model implementation. Otherwise, the predictive model is generally affected by a greater evaluation error; all these aspects are deeply discussed analysing a specific Italian case study in Chapter F, and the main results are published in [8].
The results achieved during the three years of PhD research, make it possible to affirm that each model can be used to solve thermal building balance by knowing merely a few parameters representative of the analysed problem. Nonetheless, some questions may be asked: Which of these models can be identified as the most efficient solution? Is it possible to compare the performances of these models? Is it possible to choose the most efficient model based on some specific phase in the evaluation?
To attempt to answer these questions, during the research period it was decided to compare the three selected alternative models by applying a Multi Criteria Analysis (MCA), that explicitly evaluates multiple criteria in decision-making. It is a useful decision support tool to apply to many complex decisions by choosing among several alternatives. The idea rising thanks to the scientific collaboration with the VGTU University of Vilnius, Lithuanian, in the person of Prof. A. Kaklauskas and Prof. L. Tupènaitè, experts in the field of multi-criteria analysis. At the first time a multi-criteria procedure was applied to determine the most efficient alternative model among some resolution procedures of a building’s energy balance. This application required extra effort in defining the criteria and identifying a team of experts. To apply the MCA, it was necessary to identify the salient phases of the evaluation procedure to explain the most sensitive criteria for acquiring conscious, truthful answers that only a pool of experts in the field can provide. Details of this work were carried out during the period of one-month research in Vilnius, from April to May 2019, where it was possible to improve the application of the Multiple Criteria Complex Proportional Evaluation (COPRAS) method for identifying the most efficient predictive tool to evaluate building thermal needs. These results are collected in Chapter G and the main results are explained in a paper under review in the Journal “Energy” from September.
The identification of the most efficient alternative model to solve the building energy balance through the application of a specific MCA, allowed to deepen the identified methodology and improve research. In particular, the most efficient alternative resolution model was the subject of the research that took place during the research period at the RWTH in Aachen University, Germany with Prof. M. Traverso, Head of the INaB Department, from September 2018 to March 2019. The experience in the field of LCA and the possibility of identifying the environmental impacts linked to the building system, has led the research to investigate neural networks for a dual and simultaneous environmental-energy analysis. The results confirm that the application of ANNs is a good alternative model for solving the energy and environmental balance of a building and for ensuring the development of reliable decision support tools that can be used by non-expert users. ANNs can be improved by upgrading the training database and choosing the network structure and learning algorithm. The results of this research are collected in Chapter H and published in [28]
NUMERICAL AND EXPERIMENTAL ANALYSIS OF SOLAR ASSISTED BOREHOLE THERMAL ENERGY STORAGE SYSTEMS
No full textAn analysis of 2022 data reveals that over 50% of the energy consumed in buildings is used for heating and hot water production. Furthermore, two-thirds of this energy is still generated using fossil fuels. In this context, the use of heat pumps for heating buildings, if powered by renewable electricity, could enable greater sustainability in this sector All these systems can be also integrated with solar thermal systems, in particular, ground-source heat pumps can extract heat in winter from borehole thermal energy storage produced by solar thermal collectors in the previous summer. In some cases, for example when there is little space available, these collectors can be made using road thermal collectors. In this PhD thesis, new experimental and numerical methods were developed to analyze this type of plant from an energy point of view. These methods were validated using measurements recorded in monitoring campaigns performed of a new pilot plant (SMARTEP) built at a car park on campus of University of Palermo. This plant integrates a road thermal collector and a borehole thermal energy storage and aims to demonstrate the feasibility of this type of system for heating non-residential buildings in the Mediterranean. The new method involved the construction of a pilot borehole heat exchanger and a pilot road thermal collector, both instrumented with temperature sensors, and the execution of non-conventional thermal response tests using heating cables. The interpretation of the experimental data was performed using back analysis techniques based on analytical and numerical finite element models, allowing the characterization of the thermal conductivity and diffusivity of the different soil layers and materials constituting the solar collector. In the process of this work, a new numerical model was also developed to simulate the short- and long-term response of the borehole heat exchanger. In addition, three-dimensional models of the storage and two-dimensional models of the solar collector were developed. The former were used to define g-functions of the storage and the latter were validated with a series of thermal response tests performed on the pilot plant. Finally, a numerical model of the entire SMARTEP plant was developed for the aim of simulating dynamic operation during the first 5 years of the plant's life. From results of these simulations, assumed values of storage efficiency of the new system were assessed
NET ZERO ENERGY BUILDINGS: AN ITALIAN CASE STUDY. ANALYSIS OF THE ENERGY BALANCE AND RETROFIT HYPOTHESIS IN ORDER TO REACH THE NET ZERO ENERGY TARGET.
Full text linkIn the last years the concept of Net Zero Energy Building (NZEB) has been developing and spreading in the scientific community. The work presented in this thesis has been largely developed in the context of the International Energy Agency (IEA) joint Programme Solar Heating and Cooling (SHC) Task40 and Energy Conservation in Buildings and Community Systems (ECBCS) Annex52: Towards Net Zero Energy Solar Buildings. It is known that the energy consumption in Europe for residential and commercial buildings is around 40 % of the total production. It is then extremely important to optimize both the implementation of energy efficiency measures and the usage of renewable resources that can be harvested on site. When energy efficiency measures are successfully combined with on-site renewable energy sources, and the energy consumption is equal (or nearly) to the energy production, then the output achieved can be referred to as ―near net zero energy ―or ―net zero-energy building‖. In Chapter 2 a description of the main typologies of NZEB is carried out, revealing that the most important ones are the following: site-ZEB and source-ZEB depending on where the energy balance is calculated. After a brief description of the NZEB most common definitions and classifications many examples have been examined, analyzing their features in relation to the climate in which they are, in order to show different solutions and approaches to the problem of reaching net zero energy balances (Chapter 3). In this thesis an Italian case-study has been examined: the Leaf House (LH) located in Ancona, Italy. The Leaf House is one of the best case studies of the IEA/SHC/ECBS/Task 40 Programme, in terms of thermo-physical characteristics of the building envelope, thermal plant, building automation system and energy monitoring. In Chapter 5 the Leaf House case-study is described in detail as well as the model implemented into the TRNSYS software (Chapter 6), reproducing the energy production system, the thermal features of the building and comparing simulated with monitored data. Particular attention has to be paid to the Leaf House monitoring system, which allows the assessment of the building energy balance.A careful analysis of monitored data brings to search some improving strategies to reach the zero energy target. After the simulation of the real building systems (through the software TRNSYS-Chapter 6), several scenarios have been investigated to improve energy performances of the building. Moreover the implemented model has been properly calibrated. The study proposes a detailed analysis of the case-study in order to show the possible energy savings that an NZEB can achieve in comparison with a non-net zero energy building. The re-design options are then proposed and the results evaluated by TRNSYS are described in detail. The monitored situation shows an energy consumption of 37 MWh for the year 2009; although around 6 MWh are wasted in the monitoring equipment the energy production is lower than this value. A simple solution, to reach the NZEB status is moving towards a higher production: e.g. the substitution of the PV panels with higher efficient others. In this way the energy balance reaches ―zero‖ during the year. Nevertheless the problem can be solved otherwise, reducing the energy needs. In this direction, the Geothermal Heat Pump and its energy needs have been analyzed in detail. It has been verified that the COP of the machine is way lower than the declared 4.6 and that an effective 4.6 COP could lead to significant energy savings. The idea of reaching higher efficiencies led to the proposal of a different plant scheme with the exclusion of a heat exchanger to reduce as much as possible energy losses. While it is possible to obtain the NZEB status simply making a substitution of the PV panels, the investigation on further energy savings has been continued. Finally the Italian case study allow to identify the strategies to improve the energy performances of the a Near Net Zero Energy building to reach the NZEB target. It represents also an Italian reference for others who wish to build NZEBs in the Italian context. At last two annexes to this work are shown, the first shows objectives and activities of the Task 40 ECBS Programme while the second shows the Building description file created into TRNBUILD environment, in order to describe the Leaf House building envelope features
Analisi e modelli predittivi di sistemi energetici a scala regionale, provinciale e locale: studi sperimentali, modellazione e analisi parametrica.
Full text linkIl lavoro ha affrontato differenti ambiti tipici della pianificazione energetica, dell’analisi di dati e dei modelli predittivi. Tutti i predetti temi sono coniugati al fine di studiare le caratteristiche dei sistemi energetici, i bilanci energetici a scala regionale e locale, di valutare gli indicatori di efficienza dei sistemi su macro area utilizzando metodi statistici e previsionali. A tal fine sono stati applicati modelli e software predittivi basati su metodi di regressione e/o Neural Network per la predisposizione di scenari energetici a breve, medio e lungo termine. Nel complesso, il lavoro ha riguardato l’implementazione ed analisi di modelli numerici, in grado di determinare in maniera predittiva e/o statistica i quantitativi di energia producibile da fonti non programmabili e di energia consumata per vettore e settore produttivo. Sono stati altresì implementati ulteriori casi studio che riguardano il mercato elettrico e i consumi in edifici con caratteristiche di ufficio siti in diversi stati europei. Dagli studi svolti possiamo affermare che per la predizione di indicatori energetici relativi a sistemi energetici su scala regionale, provinciale e locale, i modelli con serie storiche risultano superiori ai modelli Neural Network nel caso il DataSet sia costituito da un numero basso di dati. In tal senso la predizione assume più una veste di predizione del trend che del dato effettivo. Nel caso di DataSet ben organizzato i risultati per modelli Neural Network risultano essere più affidabili e precisi, fornendo indici di bontà quanto più elevati al crescere del numero di campioni del DataSet. Si deve anche evidenziare che al crescere del campione nel DataSet, quando questo non perde le caratteristiche di serie storica, gli indici di bontà del modello Neural Network e del modello serie storiche si eguagliano a causa di fattori di periodicità e stagionalità che caratterizzano i dati energetici di macro aree come comuni, provincie e regioni. Infine, è bene evidenziare come gli indicatori di input utilizzati nelle analisi condotte e relativi a condizioni sociali, economiche, geomorfologiche, climatiche ed impiantistiche rendono i modelli, predittivi sviluppati con approccio regressivo (Neural Network e regressione logaritimica) appropriati, nella maggior parte dei casi, alla predizione dei consumi energetici e della produzione da fonti energetiche rinnovabili su scala locale, provinciale e regionale
An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data
No full textExploiting 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 describe 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 nonlinear optimization problem. The purely analytical model, the absence of data to be obtained from graphic methods or not always available in datasheets, and a new optimised 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 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 modelling 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
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