Novel Uncertainty-Aware Deep Neuroevolution Algorithm to Quantify Tidal Forecasting-Reference-Cited by-同舟云学术

Novel Uncertainty-Aware Deep Neuroevolution Algorithm to Quantify Tidal Forecasting

Published:2022-05 Issue:3 Volume:58 Page:3324-3332
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Jalali Seyed Mohammad Jafar¹^ORCID,Ahmadian Sajad²^ORCID,Noman Md Kislu³,Khosravi Abbas¹^ORCID,Islam Syed Mohammed Shamsul³^ORCID,Wang Fei⁴^ORCID,Catalao Joaao P. S.⁵^ORCID

Affiliation:

1. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, Australia

2. Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran

3. School of Science, Edith Cowan University, Joondalup, WA, Australia

4. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China

5. Faculty of Engineering, University of Porto and INESC TEC, Porto, Portugal

Funder

European Regional Development Fund

FCT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/28/9778222/09741364.pdf?arnumber=9741364

Reference30 articles.

1. Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines

2. Back-propagation neural network for long-term tidal predictions

3. A Combined Harmonic Analysis–Artificial Neural Network Methodology for Tidal Predictions

4. Prediction models for tidal level including strong meteorologic effects using a neural network

5. Forecasting tidal currents from tidal levels using genetic algorithm

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