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Alfredo Nespoli
Researcher at Polytechnic University of Milan
Publications - 24
Citations - 372
Alfredo Nespoli is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Photovoltaic system & Computer science. The author has an hindex of 4, co-authored 16 publications receiving 199 citations.
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Journal ArticleDOI
Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
Alfredo Nespoli,Emanuele Ogliari,Sonia Leva,Alessandro Massi Pavan,Adel Mellit,Vanni Lughi,Alberto Dolara +6 more
TL;DR: In this article, the authors compare the performance of two methods for the prediction of the power output of photovoltaic systems based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison.
Journal ArticleDOI
Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery
Alfredo Nespoli,Alessandro Niccolai,Emanuele Ogliari,Giovanni Perego,Elena Collino,Dario Ronzio +5 more
TL;DR: This work presents a new model to detect in real time the clouds which potentially obstruct the sunrays directed to a specific geographic target, and a novel procedure for the forecasting of the clearness sky index on the target in the fifteen minutes is proposed.
Journal ArticleDOI
Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study
Alfredo Nespoli,Marco Mussetta,Emanuele Ogliari,Sonia Leva,Luis M. Fernández-Ramírez,Pablo Garcia-Trivino +5 more
TL;DR: A methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented, addressing the specific criticalities of this environment.
Journal ArticleDOI
A New Probabilistic Ensemble Method for an Enhanced Day-Ahead PV Power Forecast
TL;DR: In this paper, a probabilistic ensemble method (PEM) was proposed to forecast the PV energy production in a three-year real case study, where the available days have been clustered according to the solar irradiation forecast.
Proceedings ArticleDOI
Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts
Alfredo Nespoli,Emanuele Ogliari,Alberto Dolara,Francesco Grimaccia,Sonia Leva,Marco Mussetta +5 more
TL;DR: Different training approaches are considered in order to improve the accuracy of the PV power prediction, with particular attention to day-ahead and intra-day forecasts.