Deep Learning Technique for Forecasting Solar Radiation and Wind Speed for Dynamic Microgrid Analysis

被引：1

作者：

Islam, Md Mainul ^{[1
]}

Shareef, Hussain ^{[2
]}

Al Hassan, Eslam Salah Fayez ^{[2
]}

机构：

[1] Univ Western Sydney, Sch Engn Design & Built Environm, Locked Bag 1797, Penrith, NSW 2751, Australia

[2] United Arab Emirates Univ, Dept Elect & Commun Engn, Coll Engn, Al Ain 15551, U Arab Emirates

来源：

PRZEGLAD ELEKTROTECHNICZNY | 2023年 / 99卷 / 04期

关键词：

Solar power; wind power; random forest method; coot algorithm; microgrid; forecasting; ENERGY MANAGEMENT; MODEL; OUTPUT;

D O I：

10.15199/48.2023.04.27

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The key variables in the development and operation of wind and solar power systems are wind speed and solar radiation. The prediction of solar and wind energy parameters is important to alleviate the effects of power generation fluctuations. Consequently, it is essential to predict renewable energy sources like solar radiation and wind speed precisely. An artificial intelligence-based random forest method is recommended in this paper to estimate wind speed and solar radiation. The number of decision trees in the random forest model is suggested to be optimised using a novel coot algorithm (CA), and the effectiveness of the CA is evaluated to that of the currently used particle swarm optimisation (PSO) method. The best forecasting data are used in this work to develop a dynamic Microgrid (MG) in MATLAB/SIMULINK. A novel binary CA is proposed to control the MG to minimize the cost. The effect of the energy storage system is also investigated during the simulation of the MG.

引用

页码：162 / 170

页数：9

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