A Novel Approach for Hydropower Real-Time Critical Data Detection

Real-time system monitoring without human intervention is an important issue nowadays. The challenge is to find the learning model that best suits each system. In hydropower systems, critical situations occur when the water reaches the spill level or the minimum exploitation level. The actual learning models use past data to detect such instances. Our approach is to build models on future data, which is more appropriate for learning from real data. Given that the current forecasting methods are well developed and have proven their performance (the RBF Regressor achieved an RMSE of 0.291 in the current work), we propose forecasting data stored within the next month and using it to build clustering and classification models. The results show that our proposed approach achieves higher classification accuracy (99.51%) and higher or comparable Precision, Recall, F-Measure, and MCC than those of other models trained on similar datasets.