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Valerio Lo Brano graduated in Nuclear Engineering at the University of Palermo in 1998. He obtained his PhD in Environmental Physics in 2003 with a dissertation on mathematical methods used in dynamic energy simulation of buildings. In 2005 he became Research Fellow in Industrial Physics at the Department of Energy and Environmental Research of the University of Palermo. In 2011 he becomes Associate Professor and in 2019 he is Full Professor in Environmental Technical Physics. From 2013 to 2019 he is the head of the master course in Energy and Nuclear Engineering at University of Palermo. In 2021 he teaches Renewable Energy Sources, Solar Energy Systems and Environmental Technical Physics. In 2019, he is appointed general manager of the ARCA consortium and in 2020 he assumes the position of President of the board of directors. The mission of Arca consortium is the application of research and the creation of innovative companies, industrial research and technology transfer, and furthermore business incubator for the University of Palermo. Current research areas are: renewable energy sources, phase change materials, energy plants and buildings, energy planning, life cycle analysis of components and services, heat transfer in building structures, weather and climate monitoring at urban scale, neural techniques for predictive models, thermal solar devices. He is author of more than 100 publications mainly in international journals and conferences.
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.
A. Buscemi; S. Guarino; G. Ciulla; V. Lo Brano. A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance. Applied Energy 2021, 303, 117681 .
AMA StyleA. Buscemi, S. Guarino, G. Ciulla, V. Lo Brano. A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance. Applied Energy. 2021; 303 ():117681.
Chicago/Turabian StyleA. Buscemi; S. Guarino; G. Ciulla; V. Lo Brano. 2021. "A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance." Applied Energy 303, no. : 117681.
Among the different renewable energy sources, solar energy shows the highest exploitation potential to satisfy a substantial portion of the worlds’ future energy demand, guaranteeing at the same time lower emissions than conventional energy providers. Much of this potential is usable thanks to Concentrating Solar Power (CSP) technologies, of which the dish-Stirling concentrator is the most efficient. Nevertheless, the production and installation phases of the dish-Stirling technology can have an environmental impact which motivated the assessment of the plant in the three dimensions of sustainability (environmental, economic and social). The present publication evaluated an existing dish-Stirling plant located in Italy with a Life Cycle Sustainability Assessment. The Life Cycle Assessment resulted in the emission of 35 tons of CO2e. The main drivers of emissions were the electronic components (16%) and the steel used for the structure (37%). Life Cycle Costing resulted in total costs of 308,467€. S-LCA resulted in working seconds for skilled and unskilled workers equal to 1,454,400 s and 1,713,600 s, respectively. The main challenges that were identified for this work were the data availability for all pillars and the comparability between the actual study and the publications already available in the relevant literature.
J.G. Backes; A. D'Amico; N. Pauliks; S. Guarino; M. Traverso; V. Lo Brano. Life Cycle Sustainability Assessment of a dish-Stirling Concentrating Solar Power Plant in the Mediterranean area. Sustainable Energy Technologies and Assessments 2021, 47, 101444 .
AMA StyleJ.G. Backes, A. D'Amico, N. Pauliks, S. Guarino, M. Traverso, V. Lo Brano. Life Cycle Sustainability Assessment of a dish-Stirling Concentrating Solar Power Plant in the Mediterranean area. Sustainable Energy Technologies and Assessments. 2021; 47 ():101444.
Chicago/Turabian StyleJ.G. Backes; A. D'Amico; N. Pauliks; S. Guarino; M. Traverso; V. Lo Brano. 2021. "Life Cycle Sustainability Assessment of a dish-Stirling Concentrating Solar Power Plant in the Mediterranean area." Sustainable Energy Technologies and Assessments 47, no. : 101444.
Losses of electricity production in photovoltaic systems are mainly caused by the presence of faults that affect the efficiency of the systems. The identification of any overheating in a photovoltaic module, through the thermographic non-destructive test, may be essential to maintain the correct functioning of the photovoltaic system quickly and cost-effectively, without interrupting its normal operation. This work proposes a system for the automatic classification of thermographic images using a convolutional neural network, developed via open-source libraries. To reduce image noise, various pre-processing strategies were evaluated, including normalization and homogenization of pixels, greyscaling, thresholding, discrete wavelet transform, and Sobel Feldman and box blur filtering. These techniques allow the classification of thermographic images of differen quality and acquired using different equipments, without specific protocols. Several tests with different parameters and overfitting reduction techniques were carried out to assess the performance of the neural networks: images acquired by unmanned aerial vehicles and ground-based operators were compared for the network performance and for the time required to execute the thermographic inspection. Our tool is based on a convolutional neural network that allows to immediately recognize a failure in a PV panel reaching a very high accuracy. Considering a dataset of 1000 images that refer to different acquisition protocols, it was reached an accuracy of 99% for a convolutional neural network with 30 min of computational time on Low Mid-Range CPU. While a dataset of 200 sectioned images, the same tool achieved 90% accuracy with a multi-layer perceptron architecture and 100% accuracy for a convolutional neural network. The proposed methodology offers an open alternative and a valid tool that improves the resolution of image classification for remote failure detection problems and that can be used in any scientific sector.
D. Manno; G. Cipriani; G. Ciulla; V. Di Dio; S. Guarino; V. Lo Brano. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images. Energy Conversion and Management 2021, 241, 114315 .
AMA StyleD. Manno, G. Cipriani, G. Ciulla, V. Di Dio, S. Guarino, V. Lo Brano. Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images. Energy Conversion and Management. 2021; 241 ():114315.
Chicago/Turabian StyleD. Manno; G. Cipriani; G. Ciulla; V. Di Dio; S. Guarino; V. Lo Brano. 2021. "Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images." Energy Conversion and Management 241, no. : 114315.
Energy consumed for air conditioning in residential and tertiary sectors accounts for a large share of global use. To reduce the environmental impacts burdening the covering of such demands, the adoption of renewable energy technologies is increasing. In this regard, this paper evaluates the energy and environmental benefits achievable by integrating a dish-Stirling concentrator into energy systems used for meeting the air conditioning demand of an office building. Two typical reference energy plants are assumed: (i) a natural gas boiler for heating purposes and air-cooled chillers for the cooling periods, and (ii) a reversible heat pump for both heating and cooling. For both systems, a dish-Stirling concentrator is assumed to operate first in electric-mode and then in a cogenerative-mode. Detailed models are adopted for plant components and implemented in the TRNSYS environment. Results show that when the concentrator is operating in electric-mode the electricity purchased from the grid decreases by about 72% for the first plant, and 65% for the second plant. Similar reductions are obtained for CO2 emissions. Even better performance may be achieved in the case of the cogenerative-mode. In the first plant, the decrease in natural gas consumption is about 85%. In the second plant, 66.7% is the percentage increase in avoided electricity purchase. The integration of the dish-Stirling system allows promising energy-saving and reduction in CO2 emissions. However, both a reduction in capital cost and financial support are needed to encourage the diffusion of this technology.
Stefania Guarino; Pietro Catrini; Alessandro Buscemi; Valerio Lo Brano; Antonio Piacentino. Assessing the Energy-Saving Potential of a Dish-Stirling Con-Centrator Integrated Into Energy Plants in the Tertiary Sector. Energies 2021, 14, 1163 .
AMA StyleStefania Guarino, Pietro Catrini, Alessandro Buscemi, Valerio Lo Brano, Antonio Piacentino. Assessing the Energy-Saving Potential of a Dish-Stirling Con-Centrator Integrated Into Energy Plants in the Tertiary Sector. Energies. 2021; 14 (4):1163.
Chicago/Turabian StyleStefania Guarino; Pietro Catrini; Alessandro Buscemi; Valerio Lo Brano; Antonio Piacentino. 2021. "Assessing the Energy-Saving Potential of a Dish-Stirling Con-Centrator Integrated Into Energy Plants in the Tertiary Sector." Energies 14, no. 4: 1163.
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.
Stefania Guarino; Alessandro Buscemi; Giuseppina Ciulla; Marina Bonomolo; Valerio Lo Brano. A dish-stirling solar concentrator coupled to a seasonal thermal energy storage system in the southern mediterranean basin: A cogenerative layout hypothesis. Energy Conversion and Management 2020, 222, 113228 .
AMA StyleStefania Guarino, Alessandro Buscemi, Giuseppina Ciulla, Marina Bonomolo, Valerio Lo Brano. A dish-stirling solar concentrator coupled to a seasonal thermal energy storage system in the southern mediterranean basin: A cogenerative layout hypothesis. Energy Conversion and Management. 2020; 222 ():113228.
Chicago/Turabian StyleStefania Guarino; Alessandro Buscemi; Giuseppina Ciulla; Marina Bonomolo; Valerio Lo Brano. 2020. "A dish-stirling solar concentrator coupled to a seasonal thermal energy storage system in the southern mediterranean basin: A cogenerative layout hypothesis." Energy Conversion and Management 222, no. : 113228.
Alessandro Buscemi; Valerio Lo Brano; Christian Chiaruzzi; Giuseppina Ciulla; Christina Kalogeri. A validated energy model of a solar dish-Stirling system considering the cleanliness of mirrors. Applied Energy 2020, 260, 1 .
AMA StyleAlessandro Buscemi, Valerio Lo Brano, Christian Chiaruzzi, Giuseppina Ciulla, Christina Kalogeri. A validated energy model of a solar dish-Stirling system considering the cleanliness of mirrors. Applied Energy. 2020; 260 ():1.
Chicago/Turabian StyleAlessandro Buscemi; Valerio Lo Brano; Christian Chiaruzzi; Giuseppina Ciulla; Christina Kalogeri. 2020. "A validated energy model of a solar dish-Stirling system considering the cleanliness of mirrors." Applied Energy 260, no. : 1.
Solar heating and cooling systems are reliable and feasible solutions among renewable energy technologies. Indeed, solar thermal devices help reduce primary energy consumption and can reduce electricity demand, thus representing one of the best options for satisfying heating and/or cooling energy supply. The Borehole Thermal Energy Storage (BTES) represents one of the best promising option among the various storage technologies, because the size of the storage can be easily extended by drilling additional boreholes and simply connecting the pipes to the existing boreholes; the overall energy efficiency of this system is about 40–60%. In this paper, the authors present an application of this technology for the heating system of a school building located in the Southern part of Italy. Two different energy schemes are presented: a school equipped with a conventional gas boiler system with radiators and the same school building with a low temperature heat pump system with fan-coils. All simulations were performed in dynamic state by using TRNSYS software. The results of the analysis assessing the energy and economic performances of the two systems highlighting the advantages of the BTES application in the context of Italian market.
D. Panno; A. Buscemi; M. Beccali; C. Chiaruzzi; G. Cipriani; G. Ciulla; V. Di Dio; V. Lo Brano; M. Bonomolo. A solar assisted seasonal borehole thermal energy system for a non-residential building in the Mediterranean area. Solar Energy 2019, 192, 120 -132.
AMA StyleD. Panno, A. Buscemi, M. Beccali, C. Chiaruzzi, G. Cipriani, G. Ciulla, V. Di Dio, V. Lo Brano, M. Bonomolo. A solar assisted seasonal borehole thermal energy system for a non-residential building in the Mediterranean area. Solar Energy. 2019; 192 ():120-132.
Chicago/Turabian StyleD. Panno; A. Buscemi; M. Beccali; C. Chiaruzzi; G. Cipriani; G. Ciulla; V. Di Dio; V. Lo Brano; M. Bonomolo. 2019. "A solar assisted seasonal borehole thermal energy system for a non-residential building in the Mediterranean area." Solar Energy 192, no. : 120-132.
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.
A. D'Amico; G. Ciulla; M. Traverso; V. Lo Brano; E. Palumbo. Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study. Journal of Cleaner Production 2019, 239, 117993 .
AMA StyleA. D'Amico, G. Ciulla, M. Traverso, V. Lo Brano, E. Palumbo. Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study. Journal of Cleaner Production. 2019; 239 ():117993.
Chicago/Turabian StyleA. D'Amico; G. Ciulla; M. Traverso; V. Lo Brano; E. Palumbo. 2019. "Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study." Journal of Cleaner Production 239, no. : 117993.
The retrofit of urban lighting systems is often an advantageous means of achieving notable energy savings and improvements in the quality of light. User habits, expectations and lifestyle can contribute to the design of these systems, for example in deciding on the most appropriate control strategies or the light quality. The influence of such variables can be extended to the overall system performance. This paper presents a method of street lighting design based on two kinds of analysis carried out in a defined test area: measurements (by means of a monitoring study) and user preferences (by means of a survey). The results of this data analysis create the basis for the final design step. The proposed method is applied in a case study: the lighting system of the outdoor areas of the University of Palermo campus (Italy). The results show that the proposed method can be used to optimise the performance of a lighting plant in terms of energy saving and quality of light, while also taking into account the opinions of local users. Indeed, the results of the survey show that 81% of the users feel safe in test areas, 80% declare very good satisfaction with the project, and the totality of the sample would like the implementation of new features embedded in the lighting infrastructure. Expected results of energy savings in the entire campus will be about 70% with a short investment return time (3–4 years).
M. Beccali; M. Bonomolo; V. Lo Brano; G. Ciulla; V. Di Dio; F. Massaro; S. Favuzza. Energy saving and user satisfaction for a new advanced public lighting system. Energy Conversion and Management 2019, 195, 943 -957.
AMA StyleM. Beccali, M. Bonomolo, V. Lo Brano, G. Ciulla, V. Di Dio, F. Massaro, S. Favuzza. Energy saving and user satisfaction for a new advanced public lighting system. Energy Conversion and Management. 2019; 195 ():943-957.
Chicago/Turabian StyleM. Beccali; M. Bonomolo; V. Lo Brano; G. Ciulla; V. Di Dio; F. Massaro; S. Favuzza. 2019. "Energy saving and user satisfaction for a new advanced public lighting system." Energy Conversion and Management 195, no. : 943-957.
The climate of the Arabian Peninsula is characterized by significant spatial and temporal variations, due to its complex topography and the large-scale atmospheric circulation. Furthermore, the role of dust in the formation of regional climate is considered to be crucial. In this work, the regional climatology for the Arabian Peninsula has been studied by employing a high resolution state of the art atmospheric model that included sophisticated physical parameterization schemes and online treatment of natural aerosol particles. The simulations covered a 30-year period (1986–2015) with a temporal resolution of 3 h and a spatial distance of 9 km. The main focus was given to the spatial and temporal variations of mean temperature and temperature extremes, wind speed and direction, and relative humidity. The results were evaluated using in situ measurements indicating a good agreement. An examination of possible climatic changes during the present climate was also performed through a comprehensive analysis of the trends of mean temperature and temperature extremes. The statistical significant trend values were overall positive and increased over the northwestern parts of the examined area. Similar spatial distributions were found for the daily minimum and maximum temperatures. Higher positive values emerged for the daily maxima.
Platon Patlakas; Christos Stathopoulos; Helena Flocas; Christina Kalogeri; George Kallos. Regional Climatic Features of the Arabian Peninsula. Atmosphere 2019, 10, 220 .
AMA StylePlaton Patlakas, Christos Stathopoulos, Helena Flocas, Christina Kalogeri, George Kallos. Regional Climatic Features of the Arabian Peninsula. Atmosphere. 2019; 10 (4):220.
Chicago/Turabian StylePlaton Patlakas; Christos Stathopoulos; Helena Flocas; Christina Kalogeri; George Kallos. 2019. "Regional Climatic Features of the Arabian Peninsula." Atmosphere 10, no. 4: 220.
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).
G. Ciulla; A. D'Amico; V. Lo Brano; M. Traverso. Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level. Energy 2019, 176, 380 -391.
AMA StyleG. Ciulla, A. D'Amico, V. Lo Brano, M. Traverso. Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level. Energy. 2019; 176 ():380-391.
Chicago/Turabian StyleG. Ciulla; A. D'Amico; V. Lo Brano; M. Traverso. 2019. "Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level." Energy 176, no. : 380-391.
The power curve of a wind turbine describes the generated power versus instantaneous wind speed. Assessing wind turbine performance under laboratory ideal conditions will always tend to be optimistic and rarely reflects how the turbine actually behaves in a real situation. Occasionally, some aerogenerators produce significantly different from nominal power curve, causing economic losses to the promoters of the investment. Our research aims to model actual wind turbine power curve and its variation from nominal power curve. The study was carried out in three different phases starting from wind speed and related power production data of a Senvion MM92 aero-generator with a rated power of 2.05 MW. The first phase was focused on statistical analyses, using the most common and reliable probability density functions. The second phase was focused on the analysis and modelling of real power curves obtained on site during one year of operation by fitting processes on real production data. The third was focused on the development of a model based on the use of an Artificial Neural Networks that can predict the amount of delivered power. The actual power curve modelled with a multi-layered neural network was compared with nominal characteristics and the performances assessed by the turbine SCADA. For the studied device, deviations are below 1% for the producibility and below 0.5% for the actual power curves obtained with both methods. The model can be used for any wind turbine to verify real performances and to check fault conditions helping operators in understanding normal and abnormal behaviour.
G. Ciulla; A. D’Amico; V. Di Dio; V. Lo Brano. Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks. Renewable Energy 2019, 140, 477 -492.
AMA StyleG. Ciulla, A. D’Amico, V. Di Dio, V. Lo Brano. Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks. Renewable Energy. 2019; 140 ():477-492.
Chicago/Turabian StyleG. Ciulla; A. D’Amico; V. Di Dio; V. Lo Brano. 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks." Renewable Energy 140, no. : 477-492.
Artificial lighting systems have to ensure appropriate illuminance with high energy efficiency according to best design practice and technical standards. These aims can be tackled, by incorporating a Daylight linked control system. However, the system behaviour is strongly influenced by several factors and, in particular, by the sensors’ position. Indeed, very often the illuminance on work-plane is not fully correlated with illuminance measured by the photo-sensor used to control the luminaires. This fact leads to wrong information for the Daylight linked control systems affecting its efficacy. The artificial intelligence of Neural Networks can be exploited to provide a method for finding good relationships between the illuminance on workplane and the one measured in another surface. Artificial Neural Networks are able to process complex data set and to give as output the illuminance in a point. By the use of measured values in an experimental set up, the output of several Artificial Neural Networks related to different sensors placements have been analysed. In this way it was possible to find the position of the photo-sensor associated to the best forecast of the workplane illuminance with a mean square error of 2.20E-3 and R2 of 0.9583.
M. Beccali; M. Bonomolo; G. Ciulla; V. Lo Brano. Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks. Energy 2018, 154, 466 -476.
AMA StyleM. Beccali, M. Bonomolo, G. Ciulla, V. Lo Brano. Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks. Energy. 2018; 154 ():466-476.
Chicago/Turabian StyleM. Beccali; M. Bonomolo; G. Ciulla; V. Lo Brano. 2018. "Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks." Energy 154, no. : 466-476.
Italy is celebrated in the world for its agri-food industries while the process of production of pasta is highly energy demanding and requires both electrical and thermal energy simultaneously. Because most of the Italian factories producing pasta are located in the Southern part of the country, the direct use of thermal energy generated from the sun would be particularly profitable. In this study, the authors examine the possibility of generating by a Solar Industrial Process Heating plant the thermal energy required annually by an existing factory that produces durum wheat pasta located in Sicily (Italy). The hypothesized plant scheme consists of an array of Fresnel linear solar collectors and a concrete thermal energy storage system in which a heat transfer diathermal fluid circulates. This particular combination, although not the most efficient from the thermodynamic point of view, determines a lower visual impact and easier maintenance during the life span of the system. The use of food graded thermal oil ensures a high level of safety. A TRNSYS model has been developed in order to simulate the energy performance of the above described plant with the aim of optimizing the design of the solar heat for industrial process systems in terms of solar collectors and thermal energy storage dimensions taking into account the available space in the specific location. The obtained results show that the direct use of the thermal energy generated with the Fresnel solar collectors can significantly contribute to increase the sustainability of the most thermal energy-demanding factories working in the food industry, a strategic sector in the Mediterranean Area. The average annual solar contribution can reach about 40% of the total thermal energy requirement, maximizing the solar energy production during the summer season. Moreover, the proposed study allowed the determination of the maximum investment cost of the plant linked to a simple payback time, without external incentives, of 8 years.
A. Buscemi; D. Panno; G. Ciulla; M. Beccali; V. Lo Brano. Concrete thermal energy storage for linear Fresnel collectors: Exploiting the South Mediterranean’s solar potential for agri-food processes. Energy Conversion and Management 2018, 166, 719 -734.
AMA StyleA. Buscemi, D. Panno, G. Ciulla, M. Beccali, V. Lo Brano. Concrete thermal energy storage for linear Fresnel collectors: Exploiting the South Mediterranean’s solar potential for agri-food processes. Energy Conversion and Management. 2018; 166 ():719-734.
Chicago/Turabian StyleA. Buscemi; D. Panno; G. Ciulla; M. Beccali; V. Lo Brano. 2018. "Concrete thermal energy storage for linear Fresnel collectors: Exploiting the South Mediterranean’s solar potential for agri-food processes." Energy Conversion and Management 166, no. : 719-734.
Marco Beccali; Valerio Lo Brano; Marina Bonomolo; Paolo Cicero; Giacomo Corvisieri; Marco Caruso; Francesco Gamberale. A Multifunctional Public Lighting Infrastructure, Design and Experimental Test. Journal of Sustainable Development of Energy, Water and Environment Systems 2017, 5, 608 -625.
AMA StyleMarco Beccali, Valerio Lo Brano, Marina Bonomolo, Paolo Cicero, Giacomo Corvisieri, Marco Caruso, Francesco Gamberale. A Multifunctional Public Lighting Infrastructure, Design and Experimental Test. Journal of Sustainable Development of Energy, Water and Environment Systems. 2017; 5 (4):608-625.
Chicago/Turabian StyleMarco Beccali; Valerio Lo Brano; Marina Bonomolo; Paolo Cicero; Giacomo Corvisieri; Marco Caruso; Francesco Gamberale. 2017. "A Multifunctional Public Lighting Infrastructure, Design and Experimental Test." Journal of Sustainable Development of Energy, Water and Environment Systems 5, no. 4: 608-625.
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%
Giuseppina Ciulla; Antonino D’Amico; Valerio Lo Brano. Evaluation of building heating loads with dimensional analysis: Application of the Buckingham π theorem. Energy and Buildings 2017, 154, 479 -490.
AMA StyleGiuseppina Ciulla, Antonino D’Amico, Valerio Lo Brano. Evaluation of building heating loads with dimensional analysis: Application of the Buckingham π theorem. Energy and Buildings. 2017; 154 ():479-490.
Chicago/Turabian StyleGiuseppina Ciulla; Antonino D’Amico; Valerio Lo Brano. 2017. "Evaluation of building heating loads with dimensional analysis: Application of the Buckingham π theorem." Energy and Buildings 154, no. : 479-490.
The public buildings sector represents one of the most intensive items of EU energy consumption; the application of retrofit solutions in existing buildings is a crucial way to reduce its impact. To facilitate the knowledge of the energy performance of existing non-residential buildings and the choice of the more adequate actions, Public Administrations (PA) should have the availability of proper tools. Within the Italian project "POI 2007-13", a database and a decision support tool, for easy use, even to a non-technical user, have been developed. A large set of data, obtained from the energy audits of 151 existing public buildings located in four regions of South Italy have been analysed, elaborated, and organised in a database. This was used to identify the best architectures of two ANNs and to train them. The first ANN provides the actual energy performance of any building; the second ANN assesses key economic indicators. A decision support tool, based on the use of these ANNs is conceived for a fast prediction of the energy performance of buildings and for a first selection of energy retrofit actions that can be applied
Marco Beccali; Giuseppina Ciulla; Valerio Lo Brano; Alessandra Galatioto; Marina Bonomolo. Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy. Energy 2017, 137, 1201 -1218.
AMA StyleMarco Beccali, Giuseppina Ciulla, Valerio Lo Brano, Alessandra Galatioto, Marina Bonomolo. Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy. Energy. 2017; 137 ():1201-1218.
Chicago/Turabian StyleMarco Beccali; Giuseppina Ciulla; Valerio Lo Brano; Alessandra Galatioto; Marina Bonomolo. 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy." Energy 137, no. : 1201-1218.
The paper addresses the issue of the transition from a traditional electrical system without automation to a newer active and smart system allowing the possibility of implementing Demand Side Management policies, for little islands not supplied by the main grid. In particular, the paper focuses on two main topics related to the definition of:
G. Zizzo; M. Beccali; M. Bonomolo; B. Di Pietra; M.G. Ippolito; D. La Cascia; G. Leone; V. Lo Brano; F. Monteleone. A feasibility study of some DSM enabling solutions in small islands: The case of Lampedusa. Energy 2017, 140, 1030 -1046.
AMA StyleG. Zizzo, M. Beccali, M. Bonomolo, B. Di Pietra, M.G. Ippolito, D. La Cascia, G. Leone, V. Lo Brano, F. Monteleone. A feasibility study of some DSM enabling solutions in small islands: The case of Lampedusa. Energy. 2017; 140 ():1030-1046.
Chicago/Turabian StyleG. Zizzo; M. Beccali; M. Bonomolo; B. Di Pietra; M.G. Ippolito; D. La Cascia; G. Leone; V. Lo Brano; F. Monteleone. 2017. "A feasibility study of some DSM enabling solutions in small islands: The case of Lampedusa." Energy 140, no. : 1030-1046.
Oleg Gaidai; Chunyan Ji; Christina Kalogeri; Junliang Gao. RETRACTED: SEM-REV energy site extreme wave prediction. Renewable Energy 2017, 101, 894 -899.
AMA StyleOleg Gaidai, Chunyan Ji, Christina Kalogeri, Junliang Gao. RETRACTED: SEM-REV energy site extreme wave prediction. Renewable Energy. 2017; 101 ():894-899.
Chicago/Turabian StyleOleg Gaidai; Chunyan Ji; Christina Kalogeri; Junliang Gao. 2017. "RETRACTED: SEM-REV energy site extreme wave prediction." Renewable Energy 101, no. : 894-899.
Christina Kalogeri; George Galanis; Christos Spyrou; Dimitris Diamantis; Foteini Baladima; Marika Koukoula; George Kallos. Assessing the European offshore wind and wave energy resource for combined exploitation. Renewable Energy 2017, 101, 244 -264.
AMA StyleChristina Kalogeri, George Galanis, Christos Spyrou, Dimitris Diamantis, Foteini Baladima, Marika Koukoula, George Kallos. Assessing the European offshore wind and wave energy resource for combined exploitation. Renewable Energy. 2017; 101 ():244-264.
Chicago/Turabian StyleChristina Kalogeri; George Galanis; Christos Spyrou; Dimitris Diamantis; Foteini Baladima; Marika Koukoula; George Kallos. 2017. "Assessing the European offshore wind and wave energy resource for combined exploitation." Renewable Energy 101, no. : 244-264.