Solar Irradiance Prediction Based on Weather Patterns Using Bagging-Based Ensemble Learners with Principal Component Analysis

摘要

Energy production of photovoltaic (PV) system depends on the amount of solar irradiance present on a certain location. Accurate prediction of solar irradiance ensures economic integration of PV system to grid and leads to optimal dispatching of available energy resources. Weather conditions has strong correlation with solar irradiance, and its erratic nature causes fluctuation to energy production. Therefore, it is difficult to achieve consistent optimal energy production and reliable prediction of solar irradiance. In the study, a bagging-based ensemble learning system was used to predict solar irradiance based on weather patterns. Previous researches confirmed that ensemble learners produced unbiased and more accurate results compared to single learners. A pre-processed stacked long-short term memory model (stacked LSTM) was used as base learner in ensemble learning since it has good performance in handling time series sequences. A plot that compares the performance between single learner and ensemble learners was provided. From the plot, it shows that at some iteration, ensemble learners get consistent at providing more accurate predictions compared to single learners. Metrics used in the study include explained variance score, maximum residual error, mean absolute error, mean squared error, and regression score function.