Maria Malvoni

Data on Support Vector Machines (SVM) model to forecast photovoltaic power.

The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.

An Integrated Tool to Monitor Renewable Energy Flows and Optimize the Recharge of a Fleet of Plug-In Electric Vehicles in the Campus of the University of Salento: Preliminary Results

A tool has been developed to integrate electric vehicles into a general systems for the energy management and optimization of energy from renewable sources in the Campus of the University of Salento. The tool is designed to monitor the status of plug-in vehicles and recharging station and manage the recharging on the basis of the prediction of power from the photovoltaic roofs and usage of electricity in three buildings used by the Department of engineering. The tool will allow the surplus of electricity from photovoltaic to be used for the recharge of the plug-in vehicles. In the present investigation, the benefits in terms of CO2 and costs of the scheduled recharge with respect to free recharge are evaluated on the basis of the preliminary data acquired in the first stage of the experimental campaign.

Photovoltaic forecast based on hybrid PCA-LSSVM using dimensionality reducted data

The power forecasting plays a significant role in the electrical systems. Furthermore the high-dimensional data reduction without losing essential information represents an important advantage in the forecasting models. Low computational costs and short execution time together with high predicted performance are the main goals to be reached in the development of a prediction method. In this paper a hybrid method based on an active selection of the support vectors, using the quadratic Renyi entropy criteria in combination with the principal component analysis (PCA), is shown to dimensionally reduce the training data in the forecasting models. The reduced data have been used to implement the Least Squares Support Vector Machines (LS-SVM) in order to predict the photovoltaic (PV) power in the day-ahead time horizon. The model has been validated using historical data of a PV system in the Mediterranean climate. Additionally the weather variations have been taken into account to evaluate the outcome of the sunny and cloudy condition in the PV forecasting models. The proposed technique gives fulfill results. A training data size same as 30% original dimension allows to improve the forecasting accuracy and reduces the computational time of 70% respect to an implementation without dimensionality reduction data. A dimensionally reduction technique, based on the Renyi entropy and PCA is shown.LS-SVM models are applied to predict the PV output power in a one-day head frame.Photovoltaic forecasting model is performed using the historical PV power data.The predicted method considers the weather conditions as sunny and cloudy days.The accuracy and executive time of the hybrid method has been investigated.

Data on photovoltaic power forecasting models for Mediterranean climate

The weather data have a relevant impact on the photovoltaic (PV) power forecast, furthermore the PV power prediction methods need the historical data as input. The data presented in this article concern measured values of ambient temperature, module temperature, solar radiation in a Mediterranean climate. Hourly samples of the PV output power of 960kWP system located in Southern Italy were supplied for more 500 days. The data sets, given in Supplementary material File 1, were used in DOI: 10.1016/j.enconman.2015.04.078, M.G. De Giorgi, P.M. Congedo, M. Malvoni, D. Laforgia (2015) [1] to compare Artificial Neural Networks and Least Square Support Vector Machines. It was found that LS-SVM with Wavelet Decomposition (WD) outperforms ANN method. In DOI: 10.1016/j.energy.2016.04.020, M.G. De Giorgi, P.M. Congedo, M. Malvoni (2016) [2] the same data were used for comparing different strategies for multi-step ahead forecast based on the hybrid Group Method of Data Handling networks and Least Square Support Vector Machine. The predicted PV power values by three models were reported in Supplementary material File 2.

Data resulting from the CFD analysis of ten window frames according to the UNI EN ISO 10077-2

Data are related to the numerical simulation performed in the study entitled “CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077” (Malvoni et al., 2016) [1]. The paper focuses on the results from a two-dimensional numerical analysis for ten frame sections suggested by the ISO 10077-2 and performed using GAMBIT 2.2 and ANSYS FLUENT 14.5 CFD code. The dataset specifically includes information about the CFD setup and boundary conditions considered as the input values of the simulations. The trend of the isotherms points out the different impacts on the thermal behaviour of all sections with air solid material or ideal gas into the cavities.