Alessandro Pavan

150 papers receiving 6.3k citations

Alessandro Pavan's Hit Papers

Deep learning neural networks for short-term photovoltaic power forecasting 2021 · 256 citations
2560+5+10Years since publication200400600

Peers

Alessandro Pavan
Comparison fields: 5 of 162
  • Renewable Energy, Sustainability and the Environment 2.7k
  • Energy Engineering and Power Technology 314
  • Management Science and Operations Research 1.0k
  • Artificial Intelligence 2.5k
  • Electrical and Electronic Engineering 2.7k
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Countries citing papers authored by Alessandro Pavan

Since Specialization
Citations

This map shows the geographic impact of Alessandro Pavan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alessandro Pavan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alessandro Pavan more than expected).

Fields of papers citing papers by Alessandro Pavan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alessandro Pavan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alessandro Pavan. The network helps show where Alessandro Pavan may publish in the future.

Co-authors

The 25 scholars most cited alongside Alessandro Pavan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alessandro Pavan Line = papers co-authored together Alessandro Pavan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 158 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy
Hit paper breakdown →
2010699
2
A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks
Hit paper breakdown →
2016431
3 2013259
4
Deep learning neural networks for short-term photovoltaic power forecasting
Hit paper breakdown →
2021256
5 2014232
6 2020225
7 2007221
8 2019217
9 2011204
10 2006169
11 2013159
12 2004157
13 2014154
14 2017140
15 2016135
16 2009123
17 2012123
18 2014120
19 2008119
20 201194

About Alessandro Pavan

Alessandro Pavan is a scholar working on Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Economics and Econometrics, Artificial Intelligence and Management Science and Operations Research, having authored 158 papers that have together received 6.5k indexed citations. Recurring topics across this work include Photovoltaic System Optimization Techniques (48 papers), Solar Radiation and Photovoltaics (31 papers), Economic theories and models (27 papers), Auction Theory and Applications (22 papers), Game Theory and Applications (17 papers), Advanced Battery Technologies Research (16 papers), Electric Vehicles and Infrastructure (15 papers) and Smart Grid Energy Management (12 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (2.7k citations), Energy Engineering and Power Technology (314 citations), Management Science and Operations Research (1.0k citations), Artificial Intelligence (2.5k citations) and Electrical and Electronic Engineering (2.7k citations). Alessandro Pavan has collaborated with scholars based in Italy, Algeria and United States. Frequent co-authors include A. Mellit, Vanni Lughi, Christian Hellwig, Giorgio Sulligoi, Ilya Segal, Juuso Toikka, Emanuèle Ogliari, M. Benghanem, Sonia Leva and George-Marios Angeletos. Their work appears in journals such as Energies, Solar Energy, Energy Conversion and Management, Journal of Economic Theory and Sustainable Energy Grids and Networks.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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