Ali Agga

657 citations
7 papers · 474 · 1 hit paper · h-index 4

Impact in

Papers in

Ali Agga

7 papers receiving 462 citations

Ali Agga's Hit Papers

CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production 2022 · 255 citations
2550+1+2Years since publication50100150200250

Peers

Ali Agga
Comparison fields: 5 of 68
  • Energy Engineering and Power Technology 38
  • Renewable Energy, Sustainability and the Environment 148
  • Artificial Intelligence 270
  • Electrical and Electronic Engineering 332
  • Management Science and Operations Research 44
Replace Yassine El Houm with:
Yassine El Houm Morocco
Khalil Benmouiza Algeria
Zhen Hao China
Zhiming Xuan China
M. Ghayekhloo Iran
Mucun Sun United States
Bixuan Gao China
Huixin Ma China
Xavier Serrano‐Guerrero Ecuador
Luca Massidda Italy
Ali Agga relative to Yassine El Houm Morocco Yassine El Houm's profile →
Citations per field
00.5×1.5×
Yassine El Houm · 1×
Citations per year

Countries citing papers authored by Ali Agga

Since Specialization
Citations

This map shows the geographic impact of Ali Agga'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 Ali Agga with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ali Agga more than expected).

Fields of papers citing papers by Ali Agga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ali Agga. 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 Ali Agga. The network helps show where Ali Agga may publish in the future.

Co-authors

The 7 scholars most cited alongside Ali Agga, 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 Ali Agga Line = papers co-authored together Ali Agga links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown
#Work
1
CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production
Hit paper breakdown →
2022255
2 2021192
3 202115
4 20216
5 20243
6 20232
7 20221

About Ali Agga

Ali Agga is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Renewable Energy, Sustainability and the Environment, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 474 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (5 papers), Photovoltaic System Optimization Techniques (3 papers), Solar Radiation and Photovoltaics (3 papers), Smart Grid Energy Management (2 papers), Optimal Power Flow Distribution (1 paper), Microgrid Control and Optimization (1 paper), Stock Market Forecasting Methods (1 paper) and Image and Signal Denoising Methods (1 paper). The work is most often cited by research in Energy Engineering and Power Technology (38 citations), Renewable Energy, Sustainability and the Environment (148 citations), Artificial Intelligence (270 citations), Electrical and Electronic Engineering (332 citations) and Management Science and Operations Research (44 citations). Ali Agga has collaborated with scholars based in Morocco, Saudi Arabia and Egypt. Frequent co-authors include Ahmed Abbou, Yassine El Houm, Moussa Labbadi, Mohammed Ouassaid, Mohamed Maâroufi, Ali Elrashidi and Hossam Kotb. Their work appears in journals such as Electric Power Systems Research, Renewable Energy, Frontiers in Energy Research and International journal of intelligent engineering and systems.

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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