Tidal Current and Level Uncertainty Prediction via Adaptive Linear Programming

Abstract

Short-term uncertainty prediction modeling of tidal power generation supports power systems in reserve and regulation markets. In tidal power generation via various tidal energy harvesting technologies, tidal current and level are the most influential factors. This paper addresses a nonparametric prediction interval (NPI)-based uncertainty model thereof. The proposed model adapts a bi-level optimization formulation, based on extreme learning machine (ELM) prediction engine and quantile regression (QR). The quantile probabilities are asymmetrically and adaptively chosen in the upper level optimization to make prediction intervals sharper for a specific reliability level (RL). Besides, the training process of ELM is improved by adaptively selecting ELM's hidden neurons via upper level optimization. The lower level optimization finds ELM's output weighting coefficients through linear programming of QR. The heuristic optimization, consisting of gray wolf optimizer and simplex method, is designed to facilitate the NPI with high exploration and exploitation capabilities in upper level optimization. The performance of the proposed NPI is examined using empirical data recorded in three different sites, located in North America. The results of case studies show that the proposed NPI can provide sharper PIs in comparison to the well-tailored rival models whilst a prespecified RL criterion is met.