Paper “Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecast“, by M. Pavlov-Kagadejev, L. Jovanovic, N. Bacanin, M. Deveci, M. Zivkovic, M. Tuba, I. Strumberger & W. Pedrycz has been published in journal Artificial Intelligence Review!

The paper proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis.