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...
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| Other Authors: | |
| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/88844 http://hdl.handle.net/10220/45996 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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