An evolving interval type-2 fuzzy inference system for renewable energy prediction intervals

Renewable energy is fast becoming a mainstay in today’s energy scenario. Some of the main sources of renewable engery are wind, solar in addition to waves,tides,etc. These renewable energy-based production, is however inefficient from a practical as well as financial standpoint. The main reason is being the inability to forecast the exact energy that could be generated. This thesis develops a forecasting approach using interval type-2 fuzzy inferences system to address prediction intervals. The system has been adapted employing a gradient descent learning algorithm and an extended kalman filtering method. Meta-cognition is integrated into the system to improve the learning ability and prevent over-fitting. The proposed systems are used in two real-world renewable energy problems: wind and wave prediction. The wave measurement data were collected from directional waveriders deployed offshore Singapore. The experiments are then conducted on the wave energy characteristics and wind speed forecasting problems. Doctor of Philosophy