| Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry RJ Bessa, C Möhrlen, V Fundel, M Siefert, J Browell, S Haglund El Gaidi, ... Energies 10 (9), 1402, 2017 | 111 | 2017 |
| Wind power prediction in Germany–Recent advances and future challenges B Lange, K Rohrig, B Ernst, F Schlögl, Ü Cali, R Jursa, J Moradi European Wind Energy Conference, Athens 1 (5), 73-81, 2006 | 95 | 2006 |
| Exploring the offshore wind energy potential of Turkey based on multi-criteria site selection M Argin, V Yerci, N Erdogan, S Kucuksari, U Cali Energy Strategy Reviews 23, 33-46, 2019 | 86 | 2019 |
| Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland M Deveci, U Cali, S Kucuksari, N Erdogan Energy 198, 117317, 2020 | 71 | 2020 |
| Techno-economic analysis of high potential offshore wind farm locations in Turkey U Cali, N Erdogan, S Kucuksari, M Argin Energy strategy reviews 22, 325-336, 2018 | 58 | 2018 |
| Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools M Kuzlu, U Cali, V Sharma, Ö Güler IEEE Access 8, 187814-187823, 2020 | 56 | 2020 |
| Energy policy instruments for distributed ledger technology empowered peer-to-peer local energy markets U Cali, O Cakir IEEE access 7, 82888-82900, 2019 | 45 | 2019 |
| Towards the decentralized revolution in energy systems using blockchain technology U Cali, A Fifield Int. J. Smart Grid Clean Energy 8 (3), 245-256, 2019 | 43 | 2019 |
| Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA SZ Muhammet Deveci, Nuh Erdogan, Umit Cali, Joseph Stekli Engineering Applications of Artificial Intelligence 103, 2021 | 38 | 2021 |
| Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection U Cali, V Sharma Int. J. Smart Grid Clean Energy 8 (2), 103-110, 2019 | 33 | 2019 |
| Artificial neural network based wind power forecasting using a multi-model approach U Cali, B Lange, J Dobschinski, M Kurt, C Moehrlen, B Ernst Proceedings of the 7th International Workshop on Large-Scale Integration of …, 2008 | 31 | 2008 |
| Impact of electrical topology, capacity factor and line length on economic performance of offshore wind investments S Kucuksari, N Erdogan, U Cali Energies 12 (16), 3191, 2019 | 29 | 2019 |
| Use of electronic-based power conversion for distributed and renewable energy sources UCCA Zacharias, Peter ISET, 2008 | 25* | 2008 |
| The enlightening role of explainable artificial intelligence in chronic wound classification S Sarp, M Kuzlu, E Wilson, U Cali, O Guler Electronics 10 (12), 1406, 2021 | 23* | 2021 |
| Wind power forecasting B Lange, K Rohrig, F Schlögl, Ü Cali, R Jursa Renewable electricity and the grid, 95-120, 2007 | 22* | 2007 |
| Realizing the potential of blockchain technology in smart grid applications M Kuzlu, S Sarp, M Pipattanasomporn, U Cali 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies …, 2020 | 21 | 2020 |
| Energizing microgrids with electric vehicles during emergencies—Natural disasters, sabotage and warfare TS Ustun, U Cali, MC Kisacikoglu 2015 IEEE International Telecommunications Energy Conference (INTELEC), 1-6, 2015 | 21* | 2015 |
| Wind power forecasting in Germany. Recent advances and future challenges B Lange, K Rohrig, B Ernst, F Schlögl, Ü Cali, R Jursa, J Moradi Zeitschrift für Energiewirtschaft 30, 2006 | 21 | 2006 |
| Grid and market integration of large-scale wind farms using advanced wind power Forecasting: technical and energy economic aspects Ü Cali kassel university press GmbH, 2011 | 20 | 2011 |
| Strategies for balancing wind power in Germany B Lange, Ü Cali, R Jursa, R Mackensen, K Rohrig, F Schlögl German Wind Energy Conference DEWEK, 2006 | 19 | 2006 |
