Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88844
Title: An evolving interval type-2 fuzzy inference system for renewable energy prediction intervals
Authors: Nguyen, Trong Trung Anh
Keywords: DRNTU::Engineering::Materials
Issue Date: 2018
Source: Nguyen, T. T. A. (2018). An evolving interval type-2 fuzzy inference system for renewable energy prediction intervals. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
URI: https://hdl.handle.net/10356/88844
http://hdl.handle.net/10220/45996
DOI: 10.32657/10220/45996
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Theses

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