Design an interactive decision-support platform for offshore renewable energy managements

Talk
In session Digital Twins , Sept. 4, 2025, 10:45 – 11:45
Exact timing: 11:30 – 11:45
Room info: Lecture Hall

The rapid growth of offshore wind energy requires effective decision-support tools to optimize operations and manage risks. To address this, we developed iSeaPower, a web-based platform designed to support decision-making in offshore renewable energy tasks through real-time data analysis and interactive visualizations. iSeaPower integrates detailed meteorological and oceanographic data with advanced statistical methods, machine learning forecasts, and data assimilation techniques. This integration enables accurate predictions of weather windows, thorough risk assessments, and efficient operational planning for offshore wind energy stakeholders. iSeaPower is designed to optimize journey planning by considering weather conditions and travel duration. The current framework includes five methods tailored to different operational requirements. First, the forecasting method evaluates wind speed and wave height risks over short-term windows (1–3 days) using real-time weather data to quickly identify potential hazards. Second, historical database analysis calculates exceedance probabilities based on 30-day intervals from long-term historical data, revealing recurring weather risk patterns. Third, the delay time estimation method determines potential task delays across the entire year by analyzing monthly weather trends, supporting long-term operational planning and risk management. Fourth, machine learning approaches enhance the accuracy of seven-day forecasts by combining historical data with machine learning, improving short-term predictions. Finally, the updated statistics method with Monte Carlo simulation uses historical weather distributions and a forecasting operation system to provide probabilistic assessments of risks over 14-day periods. The framework produces detailed reports outlining exceedance probabilities, optimal travel windows, and clearly categorized risk levels to support informed decision-making.