Monitoring Hydropower Plants with LSTM-Based Time-series Forecasting

Data analysis and intelligent monitoring hold notable significance in the new generation of industry, aiming to enhance predictive maintenance and fault detection. This study introduces a predictive system for control within hydropower plants. The approach involves the integration of data gathered from various sensors, based on data processing techniques and deep learning (DL) algorithms. The study explores the efficacy of Long Short-Term Memory (LSTM) algorithms, renowned for their precision in time series prediction, across two different structural configurations.