Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting

TL;DR

This paper introduces a novel ensemble approach combining support vector regression forecasts with a random forest to improve day-ahead solar power predictions, utilizing multiple models and meteorological data.

Contribution

It proposes a new ensemble method that integrates multiple solar power forecasts and meteorological data using random forest, enhancing forecast accuracy.

Findings

The ensemble method outperforms individual models in accuracy.

Incorporating past forecasts and meteorological data improves predictions.

The approach is validated over a full year of data.

Abstract

To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.