Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis

As renewable energy has become increasingly popular worldwide, while solar and wind been the leading source of up to now, accuracy forecasts is challenge for planning, management, operations power system. However, due intermediate frenzied nature data, this a most challenging task. This study provides comprehensive complete review forecast based on different machine learning algorithms explore effectiveness, efficiency, competence, application potential. In work, we have built time series forecasting model with Support Vector Machine (SVM), Linear Regression (LR), Long Short-Term Memory (LSTM) twelve (12) countries. The experimental results are very interesting. For example, SVM better fit countries small mean standard deviation linear regression-based methods show bit result in case larger deviation. Meanwhile, LSTM models provide smoother regular-shaped forecasting. We can two years daily production these models. point should be noted that developed able reach Root Mean Square (RMS) value 3.1 38 model.