Optimized-XG boost learner based bagging model for photovoltaic power forecasting

As the world is aware of the problem of greenhouse gas emissions, the trend of generating energy source has been changing from conventional fossil fuels to sustainable energy such as solar and wind. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased. However, renewable energy sources highly depend on weather conditions and it has intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and that is why it is essential to have accurate forecasting technology of renewable energy to address this problem. We proposed a bagging model which is using an ensemble model as a base learner and what we set for the base learner is a XGBoost. Results showed that ensemble learner-based bagging models averagely have lower error compared to the bagging model using single model learner. Through the use of accurate forecasting technology, we will be able to reduce uncertainties in the power system and expect improved system reliability.