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International Journal of Sensors, Wireless Communications and Control

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ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

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Research Article

Improved Convolutional Neural Network and Heuristic Technique based on Forecasting and Sizing of Hybrid Renewable Energy System

Author(s): Sweta Kumari, Umesh Kumar Sinha, Manish Kumar*, Sunil Kumar Jangir and Ajay Kumar Singh

Volume 12, Issue 2, 2022

Published on: 29 January, 2021

Page: [152 - 164] Pages: 13

DOI: 10.2174/2210327911666210129153927

Price: $65

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Abstract

Aims & Objectives: The fast depletion of fossil fuels and the growing awareness of environmental protection have become a concerning topic. Because of this fact, the researchers are working for a long time to generate electrical energy sources due to the intermittent nature of unconventional energy sources such as solar, wind geothermal, tidal, and biomass as a sustainable, cost-effective, and environmentally friendly alternative for conventional energy sources. These systems are interconnected and fulfill demands as well as energy storage, which subsequently formed a complex hybrid renewable energy system. Hence, forecasting of energy generationand sizing of the equipment are essential for the economic feasibility of a complex hybrid system, and also necessary for the design analysis.

Methods: In this research article, the proposed Functional Link Convolutional Neural Network (FLCNN) is applied to forecast the energy generation from the hybrid solar and wind energy system. Also, the Jaya algorithm has been applied to find the optimal sizing of the solar and wind-based hybrid renewable energy system.

Results & Discussion: The proposed method is simple in design and implementation, and it also reduces computational complexity and time. The proposed FLCNN technique has been compared with various other Machine Learning (ML) methodology, such as Convolutional Neural Network (CNN), Random Forest (RF), and Xg-Boost. In sizing, Jaya is compared with other heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cat Swarm Optimization (CSO).

Conclusion: The proposed FLCNN and Jaya optimization techniques successfully applied for tasks like energy forecasting and sizing of the renewable energy system.

Keywords: Convolutional neural network, deep learning, forecasting, hybrid renewable energy, sizing, machine learning.

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[1]
Geem ZW. Size optimization for a hybrid photovoltaic-wind energy system. Int J Electr Power Energy Syst 2012; 42(1): 448-51.
[http://dx.doi.org/10.1016/j.ijepes.2012.04.051]
[2]
Bowen A. Feldheim: Germany’s renewable village 2015.https://www.dw.com/en/feldheim-germanys-renewable-village/a-18466800
[3]
Nwulu NI, Xia X. Optimal dispatch for a microgrid incorporating renewables and demand response. Renew Energy 2017; 101: 16-28.
[http://dx.doi.org/10.1016/j.renene.2016.08.026]
[4]
Here are INDC objectives and how much it will cost 2017.
[5]
Paul A, Kumar K, Nayyar A, Saeed F, Karthigaikumar P, Tools M. Smart Sensing View project Network, Security and privacy View project Convolutional neural network based early fire detection
[6]
Alawadi S, Kumar A, Nayyar A, Alzubi J. -A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings Machine Learning from Theory to Algorithms: An Overview. J Phys 2018; 1142(1): 12012.
[7]
Maleki A, Khajeh MG, Ameri M. Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty. Int J Electr Power Energy Syst 2016; 83: 514-24.
[http://dx.doi.org/10.1016/j.ijepes.2016.04.008]
[8]
Askarzadeh A, dos Santos Coelho L. A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran. Sol Energy 2015; 112: 383-96.
[http://dx.doi.org/10.1016/j.solener.2014.12.013]
[9]
Khatib T, Mohamed A, Sopian K. A review of photovoltaic systems size optimization techniques. Renew Sustain Energy Rev 2013; 22: 454-65.
[http://dx.doi.org/10.1016/j.rser.2013.02.023]
[10]
Kumar M, Mishra SK, Choubey SK, Tripathy SS, Choubey DK, Das D. Cat Swarm Optimization based Functional Link Multilayer Perceptron for Suppression of Gaussian and Impulse Noise from Computed Tomography Images. Curr Med Imaging 2020; 16(4): 329-39.
[http://dx.doi.org/10.2174/1573405614666180903115336] [PMID: 32410536]
[11]
Venkata Rao R, Waghmare GG. A new optimization algorithm for solving complex constrained design optimization problems. Eng Optim 2016; 1-24.
[http://dx.doi.org/10.1080/0305215X.2016.1164855]
[12]
Rao RV, Rai DP, Balic J. A new optimization algorithm for parameter optimization of nano-finishing processes A new optimization algorithm for parameter optimization of nano- nishing processes. Sci Iran 2017; 24(2): 868-75.
[13]
Kaur A, Sharma S, Mishra A. Sensing period adaptation for multiobjective optimisation in cognitive radio using Jaya algorithm. Electron Lett 2017; 53(19): 1335-6.http://digital-library.theiet.org/content/journals/10.1049/el.2017.2548
[http://dx.doi.org/10.1049/el.2017.2548]
[14]
Kumar M, Mishra SK. Jaya based functional link multilayer perceptron adaptive filter for Poisson noise suppression from X-ray images. Multimedia Tools Appl 2018; 77: 24405-25.
[http://dx.doi.org/10.1007/s11042-017-5592-y]
[15]
Alzubi J, Nayyar A, Kumar A. Machine Learning from Theory to Algorithms: An Overview. Second National Conference on Computational Intelligence. 1-15.
[http://dx.doi.org/10.1088/1742-6596/1142/1/012012]
[16]
Saeed F, Paul A, Karthigaikumar P, Nayyar A. Convolutional neural network based early fire detection. Multimedia Tools Appl 2020; 79(13–14): 9083-99.
[http://dx.doi.org/10.1007/s11042-019-07785-w]
[17]
Rajkumar RK, Ramachandaramurthy VK, Yong BL, Chia DB. Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy. Energy 2011; 36(8): 5148-53.
[http://dx.doi.org/10.1016/j.energy.2011.06.017]
[18]
Kumar M, Mishra SK. Teaching learning based optimization-functional link artificial neural network filter for mixed noise reduction from magnetic resonance image. Biomed Mater Eng 2017; 28(6): 643-54.
[http://dx.doi.org/10.3233/BME-171702] [PMID: 29171969]
[19]
Pao Y. Adaptive pattern recognition and neural networks 1989.
[20]
Atia R, Yamada N. Sizing and Analysis of Renewable Energy and Battery Systems in Residential Microgrids. IEEE Trans Smart Grid 2016; 7(3): 1204-13.
[http://dx.doi.org/10.1109/TSG.2016.2519541]
[21]
Vahidinasab V. Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design. Renew Energy 2014; 66: 354-63.
[http://dx.doi.org/10.1016/j.renene.2013.12.042]
[22]
Mellit A, Kalogirou SA, Hontoria L, Shaari S. Artificial intelligence techniques for sizing photovoltaic systems: A review. Renew Sustain Energy Rev 2009; 13(2): 406-19.
[http://dx.doi.org/10.1016/j.rser.2008.01.006]
[23]
Prakash S, Gopinath NP, Suganthi J. Wind and solar energy forecasting system using artificial neural networks. Int J Pure Appl Math 2018; 118(5): 845-54.
[24]
Sanajaoba S, Fernandez E. Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy System. Renew Energy 2016; 96: 1-10.
[http://dx.doi.org/10.1016/j.renene.2016.04.069]
[25]
Gao K, Zhang Y, Sadollah A, Lentzakis A, Su R. Jaya, harmony search and water cycle algorithms for solving large-scale real-life urban traffic light scheduling problem. Swarm Evol Comput 2016; 2017(37): 58-72.
[26]
Ismail MS, Moghavvemi M, Mahlia TMI. Genetic algorithm based optimization on modeling and design of hybrid renewable energy systems. Energy Convers Manage 2014; 85: 120-30.
[http://dx.doi.org/10.1016/j.enconman.2014.05.064]
[27]
Asrari A, Wu TX, Ramos B. A Hybrid Algorithm for Short-Term Solar Power Prediction - Sunshine State Case Study. IEEE Transactions on Sustainable Energy 2017; 8(2): 582-91.
[http://dx.doi.org/10.1109/TSTE.2016.2613962]
[28]
Zhang W, Maleki A, Rosen MA. A heuristic-based approach for optimizing a small independent solar and wind hybrid power scheme incorporating load forecasting. J Clean Prod 2019; 241
[http://dx.doi.org/10.1016/j.jclepro.2019.117920]
[29]
Hong YY, Lian RC. Optimal sizing of hybrid wind/PV/diesel generation in a stand-alone power system using markov-based genetic algorithm. IEEE Trans Power Deliv 2012; 27(2): 640-7.
[http://dx.doi.org/10.1109/TPWRD.2011.2177102]
[30]
Kefayat M, Lashkar Ara A, Nabavi Niaki SA. A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Convers Manage 2015; 92: 149-61.
[http://dx.doi.org/10.1016/j.enconman.2014.12.037]
[31]
Singh K, Ranade SK, Singh C. Comparative performance analysis of various wavelet and nonlocal means based approaches for image denoising. Optik (Stuttg) 2017; 131: 423-37.http://linkinghub.elsevier.com/retrieve/pii/S0030402616313729
[http://dx.doi.org/10.1016/j.ijleo.2016.11.055]
[32]
Khan A, Javaid N. Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery Systems. Engineering 2020; 1-5.
[33]
Alshammari N, Asumadu J. Optimum unit sizing of hybrid renewable energy system utilizing harmony search, Jaya and particle swarm optimization algorithms. Sustainable Cities and Society 2020; 60: 102255.
[34]
Indian Energy Exchange Area Price iexindia.com/marketdata/areaprice.asp

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