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Maria Grazia De Giorgi

Bio: Maria Grazia De Giorgi is an academic researcher from University of Salento. The author has contributed to research in topics: Plasma actuator & Combustion. The author has an hindex of 17, co-authored 98 publications receiving 1243 citations. Previous affiliations of Maria Grazia De Giorgi include Canadian Real Estate Association.


Papers
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Journal ArticleDOI
TL;DR: In this paper, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected photovoltaic (PV) plant installed in Italy.
Abstract: An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.

188 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction.

156 citations

Journal ArticleDOI
01 Jul 2011-Energy
TL;DR: In this article, several forecast systems based on Artificial Neural Networks have been developed to predict power production of a wind farm located in a complex terrain, where geographical effects make wind speed predictions difficult.

128 citations

Journal ArticleDOI
14 Aug 2014-Energies
TL;DR: In this article, different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP), and a comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain, where the performances of Least Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods.
Abstract: A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

118 citations

Journal ArticleDOI
TL;DR: In this paper, the power output of a photovoltaic system located in Apulia-South East of Italy at different forecasting horizons, using historical output power data and performed by hybrid statistical models based on Least Square Support Vector Machines (LS-SVM) with Wavelet Decomposition (WD).

88 citations


Cited by
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01 Jan 2007

1,932 citations

01 Jan 2016

1,633 citations

Journal ArticleDOI
TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.

829 citations

Journal ArticleDOI
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
Abstract: To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

626 citations

Journal ArticleDOI
TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.

539 citations