Valerio Lo Brano

Artificial Neural Networks to Predict the Power Output of a PV Panel

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

Mini Wind Plant to Power Telecommunication Systems: A Case Study in Sicily

The ever increasing problems related to air pollution and the difficulties for power lines to reach inaccessible areas are pushing to find new solutions for powering telecommunications equipments (TLC). The renewable energy systems, although relatively expensive, have the required characteristics. The purpose of this work is to find a solution based on a low power wind turbine to serve a real telecommunication site located near Palermo, the main city of Sicily (Italy).

Forecasting the Cell Temperature of PV Modules with an Adaptive System

The need to reduce energy consumptions and to optimize the processes of energy production has pushed the technology towards the implementation of hybrid systems for combined production of electric and thermal energies. In particular, recent researches look with interest at the installation of hybrid system PV/T. To improve the energy performance of these systems, it is necessary to know the operating temperature of the photovoltaic modules. The determination of the operating temperature is a key parameter for the assessment of the actual performance of photovoltaic panels. In the literature, it is possible to find different correlations that evaluate the referring to standard test conditions and/or applying some theoretical simplifications/assumptions. Nevertheless, the application of these different correlations, for the same conditions, does not lead to unequivocal results. In this work an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. This methodology does not require any simplification or physical assumptions. In the paper is described the ANN that obtained the best performance: a multilayer perception network. The results have been compared with experimental monitored data and with some of the most cited empirical correlations proposed by different authors.

Assessment of the Operating Temperature of Crystalline PV Modules Based on Real Use Conditions

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

A numerical solution that determines the temperature field inside phase change materials: application in buildings

Abstract The use of novel building materials that contain active thermal components would be a major advancement in achieving significant heating and cooling energy savings. In the last 40 years, Phase Change Materials or PCMs have been tested as thermal mass components in buildings, and most studies have found that PCMs enhance the building energy performance. The use of PCMs as an energy storage device is due to their relatively high fusion latent heat; during the melting and/or solidification phase, a PCM is capable of storing or releasing a large amount of energy. PCMs in a wall layer store solar energy during the warmer hours of the day and release it during the night, thereby decreasing and shifting forward in time the peak wall temperature. In this paper, an algorithm is presented based on the general Fourier differential equations that solve the heat transfer problem in multi-layer wall structures, such as sandwich panels, that includes a layer that can change phase. In detail, the equations are p...

Nonlinear Black-Box Models for Short-Term Forecasting of Air Temperature in the Town of Palermo

Weather data are crucial to correctly design buildings and their heating and cooling systems and to assess their energy performances. In the intensely urbanized towns the effect of climatic parameters is further emphasized by the Urban Heat Island (UHI) phenomenon, known as the increase in the air temperature of urban areas, compared to the one measured in the extra-urban areas. The analysis of the heat island needs detailed local climate data which can be collected only by a dedicated weather monitoring system. The Department of Energy and Environmental Researches of the University of Palermo (Italy) has built up a weather monitoring system that works 24 hours per day and makes data available in real-time at the web site: http://www.dream.unipa.it/meteo . The data collected by the system have been used to implement a set of nonlinear black-box models aiming to obtain short-term forecasts of the air temperature and map them over the monitored area. By using the data recorded during the 2008 summer, the daily profiles of the hourly average temperature have been plotted for each weather station of the monitoring system, thus clearly highlighting the temperature differences between the urban and extra-urban area and the average intensity of the UHI of Palermo.

An Approach to Rank Noise Pollution in Workplaces

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

Application of Adaptive Models for the Determination of the Thermal Behaviour of a Photovoltaic Panel

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

A decision support system to compare different strategies for achieving the Italian Kyoto Protocol commitment

In order to meet the Italian Kyoto Protocol commitment, in 2002 the Interministerial Committee for Economic Planning (CIPE) approved the national allocation plan for the reduction of Greenhouse Gases (GHGs), envisioning various alternative scenarios. In this paper an extended impact assessment of the plan is accomplished and some further options are conceived and analysed in order to find an optimal trade-off among the different economic, environmental and social items at stake. After the selection of a set of independent indicators, nine different scenarios for the GHG reductions were evaluated and they were sorted according to the principles of the multicriteria analysis. The results of the evaluation process show that the preferable options are also the ones realising a good compromise between economic investment and reduction of GHG emissions. Although the model is necessarily affected by some subjective choices, it has a high level of transparency and scientific reliability.

Electrochemical energy storage mitigating impact of electric vehicle on the electric grid: Two Italian case studies

In the framework of the Italian R&D project i-Next two pilots plants were realized aiming at demonstrate benefits coming from the adoption of electrochemical energy storages (Lithium ion batteries and hydrogen) integrated with renewable energy sources. In the present study the preliminary results coming from the demo sites operations are reported paying attention to the impacts to the grid.