Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90071
Title: Wind speed intervals prediction using meta-cognitive approach
Authors: Anh, Nguyen
Prasad, Mukesh
Srikanth, Narasimalu
Sundaram, Suresh
Keywords: Wind Forecasting
Fuzzy Logic
Engineering::Computer science and engineering
Issue Date: 2018
Source: Anh, N., Prasad, M., Srikanth, N., & Sundaram, S. (2018). Wind Speed Intervals Prediction using Meta-cognitive Approach. Procedia Computer Science, 144, 23-32. doi:10.1016/j.procs.2018.10.501
Series/Report no.: Procedia Computer Science
Abstract: In this paper, an interval type-2 neural fuzzy inference system and its meta-cognitive learning algorithm for wind speed prediction is proposed. Interval type-2 neuro-fuzzy system is capable of handling uncertainty associated with the data and meta-cognition employs self-regulation mechanism for learning. The proposed system realizes Takagi-Sugeno-Kang inference mechanism and adopts a fast data-driven interval-reduction method. Meta-cognitive learning enables the network structure to evolve automatically based on the knowledge in data. The parameters are updated based on an extended Kalman filter algorithm. In addition, the proposed network is able to construct prediction intervals to quantify uncertainty associated with forecasts. For performance evaluation, a real-world wind speed prediction problem is utilized. Using historical data, the model provides short-term prediction intervals of wind speed. The performance of proposed algorithm is compared with existing state-of-the art fuzzy inference system approaches and the results clearly indicate its advantages in forecasting problems.
URI: https://hdl.handle.net/10356/90071
http://hdl.handle.net/10220/49427
ISSN: 1877-0509
DOI: 10.1016/j.procs.2018.10.501
Rights: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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