An integrated methodology for significant wave height forecasting based on multi-strategy random weighted grey wolf optimizer with swarm intelligence.

• Main Authors: Erdogan, Nuh, Dokur, Emrah, Salari, Mahdi Ebrahimi, Yuzgec, Ugur, Murphy, Jimmy
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Institution of Engineering and Technology (IET) 2024
• Subjects: Artificial intelligence; Forecasting theory; Multilayer perceptrons; Neural nets; Optimisation; Particle swarm optimisation; Wave and tidal energy; Wave power generation; North Atlantic
• Online Access: https://rgu-repository.worktribe.com/file/1931392/1/DOKUR%202024%20An%20integrated%20methodology%20%28VOR%29; https://rgu-repository.worktribe.com/output/1931392

While wave energy is regarded as one of the prominent renewable energy resources to diversify global low-carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi-strategy random weighted grey wolf optimizer (MsRwGWO) into a multi-layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep-learning based state-of-the-art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.