A novel hybrid optimization methodology to optimize the total number and placement of wind turbines P Mittal, K Kulkarni, K Mitra Renewable energy 86 (https://doi.org/10.1016/j.renene.2015.07), 133-147, 2016 | 92 | 2016 |
Comparative study of surrogate approaches while optimizing computationally expensive reaction networks SS Miriyala, P Mittal, S Majumdar, K Mitra Chemical Engineering Science 140 (https://doi.org/10.1016/j.ces.2015.09.03 …, 2016 | 84 | 2016 |
Kriging surrogate based multi-objective optimization of bulk vinyl acetate polymerization with branching A Mogilicharla, P Mittal, S Majumdar, K Mitra Materials and Manufacturing Processes 30 (4), 394-402, 2015 | 59 | 2015 |
Optimizing the number and locations of turbines in a wind farm addressing energy-noise trade-off: A hybrid approach P Mittal, K Mitra, K Kulkarni Energy conversion and management 132, 147-160, 2017 | 48 | 2017 |
Better wind forecasting using evolutionary neural architecture search driven green deep learning KN Pujari, SS Miriyala, P Mittal, K Mitra Expert Systems with Applications 214, 119063, 2023 | 39 | 2023 |
In search of flexible and robust wind farm layouts considering wind state uncertainty P Mittal, K Mitra Journal of Cleaner Production 248, 119195, 2020 | 31 | 2020 |
Determining layout of a wind farm with optimal number of turbines: A decomposition based approach P Mittal, K Mitra Journal of cleaner production 202, 342-359, 2018 | 30 | 2018 |
Many-objective optimization of hot-rolling process of steel: A hybrid approach P Mittal, I Mohanty, A Malik, K Mitra Materials and Manufacturing Processes 35 (6), 668-676, 2020 | 18 | 2020 |
Decomposition based multi-objective optimization to simultaneously determine the number and the optimum locations of wind turbines in a wind farm P Mittal, K Mitra IFAC-PapersOnLine 50 (1), 159-164, 2017 | 12 | 2017 |
A novel and efficient hybrid optimization approach for wind farm micro-siting P Mittal, K Kulkarni, K Mitra IFAC-PapersOnLine 48 (8), 397-402, 2015 | 12 | 2015 |
Multi-objective optimization of energy generation and noise propagation: A hybrid approach P Mittal, K Kulkarni, K Mitra 2016 Indian Control Conference (ICC), 499-506, 2016 | 7 | 2016 |
Convergence of machine learning with microfluidics and metamaterials to build smart materials P Mittal, KN Nampoothiri, A Jha, S Bansal International Journal on Interactive Design and Manufacturing (IJIDeM), 1-9, 2024 | 6 | 2024 |
A tanks-in-series approach to estimate parameters for lithium-ion battery models S Kolluri, P Mittal, A Subramaniam, Y Preger, V De Angelis, ... Journal of The Electrochemical Society 169 (5), 050525, 2022 | 5 | 2022 |
Optimal Long Short Term Memory Networks for long-term forecasting of real wind characteristics KN Pujari, SS Miriyala, P Mittal, K Mitra IFAC-PapersOnLine 53 (1), 648-653, 2020 | 5 | 2020 |
Energy-noise trade-off to optimize the total number and the placement of wind turbines on wind farms: A hybrid approach P Mittal, K Mitra 2017 Indian Control Conference (ICC), 129-136, 2017 | 5 | 2017 |
A fast and effective algorithm to optimize the total number and placement of wind turbines K Kulkarni, P Mittal 2014 IEEE Global Humanitarian Technology Conference-South Asia Satellite …, 2014 | 5 | 2014 |
Energy enhancement through noise minimization using acoustic metamaterials in a wind farm P Mittal, G Christopoulos, S Subramanian Renewable Energy 224, 120188, 2024 | 4 | 2024 |
Micrositing under practical constraints addressing the energy‐noise‐cost trade‐off P Mittal, K Mitra Wind Energy 23 (10), 1905-1918, 2020 | 4 | 2020 |
Determination of optimal layout of wind turbines inside a wind farm in presence of practical constraints P Mittal, K Mitra 2019 Fifth Indian Control Conference (ICC), 353-358, 2019 | 4 | 2019 |
On determining optimal number and layout of wind turbines using space decomposed cost-energy trade-off algorithm P Mittal, K Mitra 2018 Indian Control Conference (ICC), 149-154, 2018 | 4 | 2018 |