An ensemble approach for electricity price forecasting in markets with renewable energy resources

With the restructuring of formerly vertically integrated utilities, the energy market behaves like a competitive market, which has resulted in an increased focus on the formulation of forecasting techniques. The contribution of this work is twofold. Firstly, we analyze and evaluate the impact of renewable sources on price forecasts and use them in model training. Next, we propose a bootstrap aggregated-stack generalized architecture for very short-term electricity price forecasting to facilitate market participants in formulating strategies in real time. The stacking phase integrates extreme gradient boosting and random forest, which is then bagged to obtain a computationally efficient model. The final combination of feature engineering and ensemble architecture is observed to outperform the existing techniques.