A new perspective of wind speed forecasting: Multi-objective and model selection-based ensemble interval-valued wind speed forecasting system

Abstract

Wind-speed forecasting is pivotal for optimizing resource utilization and promoting sustainable development. Previous studies have predominantly focused on point and interval forecasts derived from single-valued wind speed series, failing to capture the inherent fluctuations and uncertainties in wind speed. This study introduces a novel approach: a multi-objective and model selection-based ensemble interval-valued wind speed forecasting system. The primary objective is to empower decision-makers with enhanced decision support and improved risk management. The proposed system comprises four integral components: an interval-valued wind-speed preprocessing module, a model library module, a model selection module, and a multi-objective ensemble module. First, the preprocessing module decomposes the complex interval-valued wind speed forecasting task into multiple simpler forecasting subtasks. Subsequently, a model library is constructed for different forecasting subtasks. The model selection module utilizes a comprehensive evaluation metric to select the best-suited prediction model for each forecasting subtask. Finally, the multi-objective ensemble module integrates the subtask prediction results to yield the interval-valued wind speed forecasting outcomes. The most significant findings of this study are: (1) When faced with different forecasting subtasks, the choice of the optimal prediction model varies depending on the nature of the task. Therefore, the results of the model selection also differ with changes in the data characteristics. (2) In the ensemble stage, the importance and contribution of different subtasks may differ. The proposed ensemble module considers the interrelations and interactions between subtasks, resulting in varying weights for each subtask. For the two different datasets from the Chengde wind farm, the interval mean absolute percentage error values were 2.0862 % and 3.3632 %, and the interval standard deviation values of the error were 0.1383 and 0.3206, respectively. The experimental results clearly demonstrate the exceptional performance of the proposed system in interval-valued wind speed forecasting, which provides better decision support and risk management for wind speed forecasting.