Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Abstract

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

“…Based on their findings, the proposed model based on bagging and boosting methods improved ANN, DT, and SVR in the range of 4.6 and 14.6% in terms of RMSE. Several ensemble models to predict short-term solar irradiation were investigated in [ 71 ]. The models were RF, Boosted Trees, Generalized Random Forest, and Bagged Trees.…”

“…In contrast to data diversity, parameter diversity is applied different parameter settings with the same dataset . The forecasts can be generated by using the following equation [ 71 ]: where is the forecast horizon and is the predicted value. is the parameter for model .…”

Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting

“…It was determined based on our testing and training data that Cubist models performed better than the ANN models and that there was no benefit to using regime-dependent models for this site with dominant clear sky conditions. Details of that analysis and results of experiments leading to that conclusion can be found in [32].…”

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

“…The use of a regime-dependent approach, in which k-means clustering was used to independently classify regimes before applying an ANN, led to a degraded performance. The dominance of clear sky conditions in the meteorological conditions of Kuwait makes regime-identification approaches perform worse, due to minimal cases of cloudy sky conditions, and such approaches could be better suited to climate regimes with more diverse cloud conditions [94]. Another example is provided for the Nordic climate, which is characterized by daylight hours that are long in the summer and short in the winter, heavy snow, and highly variable weather conditions due to fast-moving clouds.…”