Federico Succetti
Assegnista di ricerca
E-mail:
| Titolo | Pubblicato in | Anno |
|---|---|---|
| Reti neurali e neurofuzzy per la classificazione di serie temporali energetiche | Memorie ET2024 | 2024 |
| A neural network symbolic approach to structural health monitoring in aerospace applications | Proceedings of 2024 IEEE Congress on Evolutionary Computation (IEEE CEC 2024) | 2024 |
| Multi-label classification with imbalanced classes by fuzzy deep neural networks | INTEGRATED COMPUTER-AIDED ENGINEERING | 2024 |
| A deep learning-based approach for battery life classification | Proceedings - 24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024 | 2024 |
| Challenges and perspectives of smart grid systems in islands. A real case study | ENERGIES | 2023 |
| An adaptive embedding procedure for time series forecasting with deep neural networks | NEURAL NETWORKS | 2023 |
| Multivariate time series analysis for electrical power theft detection in the distribution grid | 2022 IEEE International conference on environment and electrical engineering and 2022 IEEE Industrial and commercial power systems Europe, EEEIC / I and CPS Europe 2022 | 2022 |
| A price-aware dynamic decision system in energy communities | 2022 IEEE International conference on environment and electrical engineering and 2022 IEEE Industrial and commercial power systems Europe, EEEIC / I and CPS Europe 2022 | 2022 |
| A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition | ELECTROMAGNETIC WAVES | 2022 |
| Nonexclusive Classification of Household Appliances by Fuzzy Deep Neural Networks | Communications in Computer and Information Science | 2022 |
| Deep learning per la predizione multivariata di serie energetiche | Memorie ET2022 | 2022 |
| 2-D Convolutional Deep Neural Network for Multivariate Energy Time Series Prediction | ENERGIES | 2021 |
| Time series prediction with autoencoding LSTM networks | Advances in Computational Intelligence (LNCS 12862) | 2021 |
| A blockwise embedding for multi-day-ahead prediction of energy time series by randomized deep neural networks | Proceedings of the International Joint Conference on Neural Networks | 2021 |
| Multivariate Prediction of Energy Time Series by Autoencoded LSTM Networks | 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings | 2021 |
| Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid | 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings | 2021 |
| Multidimensional feeding of LSTM networks for multivariate prediction of energy time series | 2020 | |
| A combined deep learning approach for time series prediction in energy environments | Conference Record - Industrial and Commercial Power Systems Technical Conference | 2020 |
| ADMM consensus for deep LSTM networks | Proceedings of the International Joint Conference on Neural Networks | 2020 |
| Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series | IEEE ACCESS | 2020 |
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