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The paper presents the design state of a smart car park shelter for electric vehicle in the campus of the University of Palermo. The car park is supplied by the main grid and by a 40 kW photovoltaic system and is equipped with a Li-ion battery storage system with capacity 42 kWh. The hybrid plant is designed in order to operate both in connection with the main grid and in stand-alone mode, supplying, in this case, all car park's loads and providing one recharge to an electric vehicle. Moreover, the energy storage system and, eventually, the battery of one or more cars with V2G technologies, can be managed for providing ancillary services to the grid. In the paper, the design of the system and the choice of all components is presented. Finally, the environmental impact of the diffusion of such installations on the energy system is discussed.

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 principles of sustainable development and of Integrated Product Policy are applied in new voluntary regulations, which have been internationally agree. The aim is to grant transparency of environmental information and to promote the diffusion of environmentally friendly products. A new tool under study is the Environmental Product Declaration (EPD), a technical paper annexed to products that synthesises their energy and environmental performances. This paper points out the reasons for developing EPD for the energy sector, and puts special focus upon renewable energy systems, products whose environmental performances are often not clearly defined. We describe how results of a LCA upon a solar thermal collector can be included in an EPD-document, summarising its ecological profile and making information comparable. EPD could be employed for a correct decisional strategy, making a balance between the benefits and the impacts of this technology, and could represent a way to diffuse scientific results to customers.

Surface energy balance of urban areas depends on the thermal features of building materials, as well as the overall urban layout and various anthropogenic factors. In extended urban areas, all these factors give rise to the phenomenon known as Urban Heat Island (UHI). From the point of view of urban health, the occurring of UHI drastically changes the fluid dynamic interaction between the air and the city surface, often preventing the dispersion of gaseous pollutants. In the last years in fact, several studies have monitored the increasing trend of breathing diseases and allergies correlated with the presence of UHI. Nowadays, a rational design of the urban areas within large towns should take into account important factors such as energy requirements, consumption of natural resources, vehicular traffic and air pollution. The study and monitoring of UHI can supply very important data useful to address a correct urban planning policy. At this aim, the authors have created an automatic monitoring system able to acquire weather data in different points of the urban area of Palermo and compare them to the data recorded in a nearby rural area. This paper describes the system setup and the first analyses accomplished with the recorded data.

In the framework of international actions to reduce the energy requirements and greenhouse gases emissions due to buildings, a new International Energy Agency task has been recently established in order to study Net Zero Energy Buildings (NZEBs). The commonly shared concept of NZEB, is a building whose annual balance of energy consumptions tends to zero. This concept is still too imprecise and the authors of this paper participate to the activities of SubTask B of IEA Task40 with the aim of establishing an internationally agreed understanding on NZEBs. The task is based on a common methodology for identifying and refining design approaches and tools to support industry adoption of innovative demand/supply technologies for NZEBS. This goal is pursued through detailed modeling and analysis of specific NZEB case studies. Among the specific objectives of the Sub Task B it is possible to include the analysis of redesigned studies. Redesigned studies should identify better alternative solutions for plants, building envelope or impact on the environment that significantly modify the building. To provide high quality information about the design process of a NZEB, it was decided to examine in detail the specific case study of the Leaf House (LH) located in Ancona, Italy. The studied building is fully monitored in terms of thermal environment, energy production and consumption, water use and occupancy. The purpose of this paper is to present some optionsto improve the performance of the selected building, identified by using the collected data and analyzing a detailed TRNSYS model of plant-building complex. The model has allowed detailed evaluation of the effects of some changes in the design that can improve the behavior of the Leaf House in terms of consumptions of energy resources and environmental impact of the building. The performed analysis shows that the building envelope is already very effective in terms of thermal performance, while the redesign of the thermal plants and the PV system should permit to reach a nearly net zero energy performance.

Nowadays, there is a great impulse in the field of photovoltaic cell systems (PV) to develop new devices with better energy conversion performances and higher cost effectiveness. This paper is focused on the design and development of a modified PV system, consisting of a normal PV panel coupled with a Phase Change Material (PCM). PCMs are "latent" energy storage materials, using chemical bonds to store and release heat. The system is based on the idea that decreasing the operating temperature of the panel causes an increase in its energy conversion efficiency. A theoretical analysis of the system was accomplished by using COMSOL MULTIPHYSICS, a partial differential equations (PDEs) solver. By using this software, the thermal behaviour of the PV-PCM system was simulated, thus allowing a better identification of the suitable thermo-physical parameters for the optimization of the panel efficiency. The numerical simulations accomplished showed that a PCM with a melting temperature between 28°C and 32°C in a typical summer day in Sicily induces a significant variation in the energy conversion efficiency. The experimental set-up realized to validate these results is also described in the paper, but the experimental tests have still not been run. Furthermore, the exploitation of this simple technology could permit to reduce CO2 emissions because the increasing of the energy conversion efficiency means more energy production and consequently less emission of greenhouse gases to produce energy by fossil fuel or normal PV panel.

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.

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.

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.

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.

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.

Nowadays, the installation of efficient lighting sources and Information and Communications Technologies can provide economic benefits, energy efficiency, and visual comfort requirements. More advantages can be derived if the public lighting infrastructure integrates a smart grid. This study presents an experimental multifunctional infrastructure for public lighting, installed in Palermo. The system is able to provide smart lighting functions (hotspot Wi-Fi, video-surveillances, car and pedestrian access control, car parking monitoring and support for environmental monitoring). A remote control and monitoring platform called “Centro Servizi” processes the information coming from different installations as well as their status in real time, and sends commands to the devices (e.g. to control the luminous flux), each one provided with a machine to machine interface. Data can be reported either on the web or on a customised app. The study has shown the efficient operation of such new infrastructure and its capability to provide new functions and benefits to citizens, tourists, and public administration. Thus, this system represents a starting point for the implementation of many other lighting infrastructure features typical of a “smart city.&rdquo

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

The paper presents the results of a study carried out on Lampedusa Island (Italy) and based on the survey of electricity consumption data. The main outcomes of the study are: • the characterization of the energy demand of private houses and hotels in Italian small islands with significant touristic flows during the summer period; • the identification of inefficient use of the electric loads; • the construction of aggregated load profiles for clusters of homogeneous domestic end-user to be used for implementing Demand Response strategies. The study shows how electricity consumption in Mediterranean small islands are very high with respect to the national average and lays the basis for future studies on the impact of automation and Renewable Energy Sources on the energy supply-demand sustainability of the island.

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

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

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).

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.

This work addresses the energy saving performances of some solar Desiccant and Evaporative Cooling (DEC) systems working with the freescoo technology. The innovative freescoo concept is based on the use two fixed and cooled adsorption beds and advanced indirect evaporative cooling processes. The main feature of this new adsorption bed concept is to allow the simultaneous dehumidification and cooling of air. The systems analyzed have been installed in Italy last here and results based on field monitoring data are here presented. A description of the monitored systems and comparisons between the energy performances based on the main performance indicators such as EER, thermal COP, cooling power, off grid operation data are shown. Systems are provided with solar PVT collectors which produce the necessary heat for the regeneration of the desiccant and fulfil most of the need of electricity.

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

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.

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.

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.

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 2.20 E−3 and R2 of 0.9583.

The large potential for energy saving by cogeneration and trigeneration in the building sector is scarcely exploited due to a number of obstacles in making the investments attractive. The analyst often encounters difficulties in identifying optimal design and operation strategies, since a number of factors, either endogenous (i.e. related with the energy load profiles) and exogenous (i.e. related with external conditions like energy prices and support mechanisms), influence the economic viability. In this paper a decision tool is adopted, which represents an upgrade of a software analyzed in previous papers; the tool simultaneously optimizes the plant lay-out, the sizes of the main components and their operation strategy. For a specific building in the hotel sector, a preliminary analysis is performed to identify the most promising plant configuration, in terms of type of cogeneration unit (either microturbine or diesel oil/natural gas-fueled reciprocate engine) and absorption chiller. Then, sensitivity analyses are carried out to investigate the effects induced by: (a) tax exemption for the fuel consumed in “efficient cogeneration” mode, (b) dynamic behavior of the prime mover and consequent capability to rapidly adjust its load level to follow the energy loads.

The installation of Building Automation Control Systems (BACs) in general is an effective action to achieve relevant energy savings. Commercial BACs, installed in residential or small offices, often include functions of lighting control, acting as Daylight-Linked Control Systems (DLCSs). Nevertheless, because system performance is strictly dependent on different parameters, BAC’s hardware and software configuration and as well as an inaccurate commissioning do not always allow a perfect execution of the desired tasks; therefore, the system could not work as expected. Moreover, it is well known that energy saving potential is specifically related to the variability of the light environment. Although many methods to assess it are available, these are mainly based on average data, tabular factors, and climate/lighting time series embedded in weather files used in detailed simulations and, in general, they adopt as a benchmark the performance of ideal control systems. In this paper, actual performances of a daylight-linked control system have been evaluated by a set of indices which, in general, can be calculated, after measurement, during the commissioning stage or in periodic monitoring of the system. In particular, these indices take into account the excess and the deficiency of illuminance over time with respect to a target set point that a lighting system should provide and the related energy consumption. The indices have been tested using data measured in a laboratory set up where a commercial daylight-linked control system, working not close to an ideal one, is installed and has been evaluated for different end uses, operating schedules, control strategy (dimming and ON/OFF) and daylight conditions. Finally, useful relations between system performance and environmental conditions have been found.

In the last few years, many buildings have been equipped with Building Automation and Control (BAC) systems. BAC systems can be used to achieve a significant reduction of the electrical consumption and to have all the well acknowledged benefits from the daylight (e.g. occupant comfort, health, well-being and productivity). For this reason, Standard EN 15232 "Energy performance of buildings — Impact of Building Automation, Controls and Building Management" puts into evidence the importance of the presence of BAC and Technical Building Management (TBM) systems, suggests a classification of these systems, explains their main functionalities and presents a simplified method to estimate their impact on the energy consumption of the building. The aim of this paper is to verify this simplified BAC factor method using the results of an experimental measurements campaign. In the paper, the BAC factors are evaluated for a residential and an office environment, using a big set of data measured for one year every 5 minutes at the laboratory of the DEIM (University of Palermo), where a BACs for lighting control is installed.