M.G. De Giorgi

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.

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.

Flow Separation Control on a Compressor-Stator Cascade Using Plasma Actuators and Synthetic and Continuous Jets

AbstractThe effects of active separation control on a highly loaded subsonic compressor stator cascade were numerically studied by comparing the behavior of different devices: continuous jet actuator (CJA), zero-net mass-flux synthetic jet actuator (SJA), and plasma actuator (PA). For the jet actuator modeling, a suction/blowing type boundary condition was used, imposing a time-constant velocity for the CJA case and a prescribed sinusoidal time-varying velocity for the SJA case. For the PA case, the body force, which represents the effect of the plasma actuator on the flow, was added to the momentum equations in the computational fluid dynamics (CFD) code. The PA is slightly more efficient for the reduction of flow separation in the region just downstream of the blade actuators. However, at the same mechanical power delivered by the actuator to the fluid, the SJAs are more advantageous than the CJAs and slightly outperform plasma actuator application from the pressure loss reduction and pressure rise view...

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.

Modeling, fabrication and plasma actuator coupling of flexible pressure sensors for flow separation detection and control in aeronautical applications

Preventing the flow separation could enhance the performance of propulsion systems and future civil aircraft. To this end, a fast detection of boundary layer separation is mandatory for a sustainable and successful application of active flow control devices, such as plasma actuators. The present work reports on the design, fabrication and functional tests of low-cost capacitive pressure sensors coupled with dielectric barrier discharge (DBD) plasma actuators to detect and then control flow separation. Finite element method (FEM) simulations were used to obtain information on the deflection and the stress distribution in different-shaped floating membranes. The sensor sensitivity as a function of the pressure load was also calculated by experimental tests. The results of the calibration of different capacitive pressure sensors are reported in this work, together with functional tests in a wind tunnel equipped with a curved wall plate on which a DBD plasma actuator was mounted to control the flow separation. The flow behavior was experimentally investigated by particle image velocimetry (PIV) measurements. Statistical and spectral analysis, applied to the output signals of the pressure sensor placed downstream of the profile leading edge, demonstrated that the sensor is able to discriminate different ionic wind velocity and turbulence conditions. The sensor sensitivity in the 0–100 Pa range was experimentally measured and it ranged between 0.0030 and 0.0046 pF Pa−1 for the best devices.

Embedded sensor/actuator system for aircraft active flow separation control

This work reports on finite element method (FEM) design and fabrication of low cost capacitive pressure sensors for aircraft applications; this work is part of a research activity aimed to develop an embedded sensor-actuator system composed by multi-measure points pressure sensors for flow turbulence detection and coupled plasma actuators for control of separated flows on aircraft wings structures. The system uses sensors feedback information to provide fast reattachment of boundary layer separation flow on the suction surface of regional aircraft vehicles. Flow separation has great impact on the performance and safety of an aircraft and it can be predicted by quantifying the pressure gradients along the wing wall. Considering the absolute pressure values on a NACA 0012 profile as a function of the angle of attack, high sensitivity measurements of differential pressure can be obtained by positioning the sensor-nodes at points on the airfoil surface where the P/Pstall ratio between the absolute pressure at different angles of attack and the pressure measured in stall condition is maximized.

Dissipated power and induced velocity fields data of a micro single dielectric barrier discharge plasma actuator for active flow control.

In recent years, single dielectric barrier discharge (SDBD) plasma actuators have gained great interest among all the active flow control devices typically employed in aerospace and turbomachinery applications [1,2]. Compared with the macro SDBDs, the micro single dielectric barrier discharge (MSDBD) actuators showed a higher efficiency in conversion of input electrical power to delivered mechanical power [3,4]. This article provides data regarding the performances of a MSDBD plasma actuator [5,6]. The power dissipation values [5] and the experimental and numerical induced velocity fields [6] are provided. The present data support and enrich the research article entitled “Optimization of micro single dielectric barrier discharge plasma actuator models based on experimental velocity and body force fields” by Pescini et al. [6].

A Data Acquisition System to Detect Bubble Collapse Time and Pressure Losses in Water Cavitation

This paper presents a data acquisition system oriented to detect bubble collapse time and pressure losses in water cavitation in an internal orifice. An experimental campaign on a cavitating flow of water through an orifice has been performed to analyze the flow behavior at different pressures and temperatures. The experiments were based on visual observations and pressure fluctuations frequency analysis. Comparing the visual observations and the spectral analysis of the pressure signals, it is evident that the behavior of the different cavitating flows can be correlated to the frequency spectrum of the upstream, downstream and differential pressure fluctuations. The further reduction of the cavitation number and the consequent increase in the width of the cavitating area are related to a corresponding significant increase of the amplitude of typical frequency components. The spectrogram analysis of the pressure signals leads to the evaluation of the bubble collapse time, also compared with the numerical results calculated by the Rayleigh-Plesset equation.

A Neural Network Approach to Analyse Cavitating Flow Regime in an Internal Orifice

The identification of the water cavitation regime is an important issue in a wide range of machines, as hydraulic machines and internal combustion engine. In the present work several experiments on a water cavitating flow were conducted in order to investigate the influence of pressures and temperature on flow regime transition. In some cases, as the injection of hot fluid or the cryogenic cavitation, the thermal effects could be important. The cavitating flow pattern was analyzed by the images acquired by the high-speed camera and by the pressure signals. Four water cavitation regimes were individuated by the visualizations: no-cavitation, developing, super and jet cavitation. As by image analysis, also by the frequency analysis of the pressure signals, different flow behaviours were identified at the different operating conditions. A useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon is the application of Artificial Neural Network (ANN). In the present study a three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. Results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence the different cavitation regimes and the impact of the temperature. A theoretical analysis has been also performed to justify the results of the experimental observations. In this approach the nonlinear dynamics of the bubbles growth have been used on an homogenous vapor-liquid mixture model, so to couple the effects of the internal dynamic bubble with the other flow parameters.© 2012 ASME