Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting

Abstract

Wind power forecasting plays a significant role to ensure the safe operation of power systems. However, due to the stochastic nature and dynamic uncertainty of wind power, accurate and stable forecasting faces challenges. In this study, a modified combined structure with secondary decomposition-model selection and non-agnostic uncertainty sampling-active learning-sample selection strategy is proposed for multi-step deterministic and probabilistic wind power forecasting. Secondary decomposition-model selection strategy is used before experiments, selecting better forecasting models from the initial model space to the selected model space, and secondary decomposition methods are applied for decreasing the interference in initial data. Non-agnostic uncertainty sampling-active learning-sample selection strategy is employed to accelerate the sample selection process and enhance the testing efficiency, indirectly promoting the final performance. To further promote the forecasting precision and stability simultaneously, an advanced multi-objective optimization is applied, automatically selecting the top best models into the combined models from the selected model space. Except deterministic forecasting, uncertainty estimation is vital, offering different aspects of forecasting information for risk management, as the grid system absorbs large-scale wind power generation, therefore, probabilistic forecasting is considered too. Multi-step forecasting is also considered. Eight datasets from Elia, Belgium, are applied to assess the forecasting performance of secondary decomposition-model selection and the proposed combined models. According to results, compared with comparison models, the proposed combined models obtain higher accuracy and stability of multi-step deterministic forecasting (improving the forecasting skill by more than 90% in accuracy and stability for 1-step to 3-step forecasting) and also offer multi-step probabilistic information.