| Deep reinforcement learning for energy management in a microgrid with flexible demand TA Nakabi, P Toivanen Sustainable Energy, Grids and Networks 25, 100413, 2021 | 173 | 2021 |
| An ANN-based model for learning individual customer behavior in response to electricity prices TA Nakabi, P Toivanen Sustainable Energy, Grids and Networks 18, 100212, 2019 | 60 | 2019 |
| Computational intelligence for demand side management and demand response programs in smart grids TA Nakabi, K Haataja, P Toivanen Proceedings of the 8th International Conference on Bioinspired Optimization …, 2018 | 8 | 2018 |
| Optimal price-based control of heterogeneous thermostatically controlled loads under uncertainty using LSTM networks and genetic algorithms TA Nakabi, P Toivanen F1000Research 8, 1619, 2019 | 3 | 2019 |
| A physical-neural network approach for residential load forecasting with dynamic load control T Nakabi, C Brester, M Kolehmainen, H Niska 27th International Conference on Electricity Distribution (CIRED 2023) 2023 …, 2023 | | 2023 |
| Computational intelligence for smart grid’s flexibility: prediction, coordination, and optimal pricing TAEH Nakabi Itä-Suomen yliopisto, 2020 | | 2020 |
| Prediction of sulfides concentration by artificial neural network S Abderafi, R Ellaia, TA Nakabi | | |
Pekka ToivanenProfessor, University of Eastern Finland在 uef.fi 的电子邮件经过验证
Pekka ToivanenProfessor, University of Eastern Finland在 uef.fi 的电子邮件经过验证
Keijo HaatajaExperienced Senior Manager Funded Projects在 uef.fi 的电子邮件经过验证