![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
The conceptual prediction approaches for solar energy and Photovoltaic energy are thoroughly reviewed in this work. Employing enhanced gated recurrent units (GRUs) and recurrent neural networks (RNNs) for both univariate and multivariate cases, this research proposes a unique technique for the forecasting of electrical load for a smart grid. Initially, many delicate tracking variables or previous power usage information are chosen for the source information following the correlation research. Furthermore, a Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) is built utilizing an enhanced learning algorithm which is premised on Adaptive Gradient and customizable velocity, employing a condensed GRU. The revised training approach and redesigned RNN-GRU architecture increase the effectiveness and durability of learning. Finally, because of its productive learning mechanisms and self-feedback interconnections, the RNN-GRU is employed to create a precise mapping between both the variables examined and Renewable production or power loads. Experimental investigations are used to verify the presented approach: one predicts power requirements utilizing previous information on electricity usage while the other predicts solar power generation utilizing a variety of meteorological characteristics. The empirical outcomes show that the suggested strategy beats cutting-edge deep learning techniques in generating a precise power forecast for an efficient smart grid
Ethics Approval and Consent to Participate
No participation of humans takes place in this implementation process.
Human and Animal Rights
No violation of Human and Animal Rights is involved.
Disclosure Statement
Conflict of Interest is not applicable in this work.
Authorship Contributions
All authors are contributed equally to this work.
Data Availability Statement
Data sharing not applicable to this article as no datasets were generated or analyzed during this study.
Additional information
Funding
Notes on contributors
Hemlata Gangwar
Hemlata Gangwar is an Associate Professor, MIT World Peace University, Pune. She has over 10 years of in academics and industry. She holds her Fellow (Ph.D.) from IIM, Mumbai India. She has done Masters in Computer Applications. Her current research interests are in the areas of Technology Adoption, Big Data Analytics, Natural Language Processing and Business Analytics. Her work has been published in Journal of Enterprise Information Management, International Journal of Quality & Reliability Management, Information Resources Management Journal, Human System Management and Electronic journal of IS Evaluation. She has also reviewed paper for top business management journals such as Industrial Management and Data system, Journal of Cloud Computing, International Journal of Information Management, Business Process Management journal, and Behavior and information Technology.
Syam Machinathu Parambil Gangadharan
Syam Machinathu Parambil Gangadharan is a passionate research scientist at the forefront of the intersection of Machine Learning and Artificial Intelligence. He is currently working as a Sr. Machine Learning Engineer at The Home Depot, USA. He received his MS in Artificial Intelligence and Machine Learning from Liverpool John Moores University, UK. He has published several research papers in peer-reviewed Journals. He is also an active reviewer for several reputed Journals. With expertise in Generative AI (GenAI), Deep Learning and Natural Language processing (NL), and cutting-edge technologies, he is dedicated to advancing the frontiers of intelligent systems.
Leena Daniel
Leena Daniel is a Head of the Department in Electrical and Electronics Engineering at Sagar Institute of Science and Technology Bhopal, Madhya Pradesh, India. She has completed her Ph.D in Electrical Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya University, Bhopal. She has published several research papers in reputed journals and conferences. Her area of research is Power System, Soft Computing Techniques, Microgrids and Renewable energy resources. She reviewed many research papers and Thesis.
B. Srinivasa Kumar
B. Srinivasa Kumar is a committed academician with experience in teaching and research. He received his Ph.d in Mathematics from koneru lakshmaiah education foundation, Guntur. Currently, he is working as an Associate professor in Mathematics, K L E F. He published several research articles in reputed journals and conferences and published three patents. Under his guidance,4 scholars are received Ph.d and 2 scholar are pursuing ph.d. He is reviewer of three journals. His area interest is Fuzzy theory, semi groups, Neutrosophic theory and Networks.
P. Hariramakrishnan
P. Hariramakrishnan received his bachelor’s degree in Electrical and Electronics engineering under Madras University, Chennai. He received his Master's degree under Anna University and Ph.D. degree under Sathyabama Institute of Science and Technology. At present he is working as Associate Professor in Panimalar Engineering College, Chennai, Tamil Nadu. His areas of interests are Power quality in power systems, Machine learning and Deep learning techniques. He has published many articles in international journals. Email: [email protected]
G. Ramkumar
G. Ramkumar obtained his Bachelor’s degree in Electronics and Communication and Master’s degree in VLSI Design from Sathyabama University, Chennai, Tamil Nadu, India. Completed his PhD in 2020 in Electronics Engineering from Sathyabama Institute of Science and Technology, India. Currently, He has more than 12 years’ experience in academic teaching and more than 10 years of experience in Industry. He has published more than 120 articles in IEEE Conference/Scopus/SCIE Journals. His area of interest is Image Processing, Human Computing Interaction, Machine Learning, Deep learning, VLSI Design and Signal Processing. He is Senior Member in IEEE, Member - the IAENG Society of Computer Science and Imaging Engineering, Senior Member – the Institute of Research Engineers and Doctors (IRED) and reviewer for Elsevier, Springer and other reputed journals. Recognized as one of the Top 2% Scientist in the World based on the survey conducted by Elsevier and Stanford University - USA.
M. Arunkumar
M. Arunkumar is a citizen of India, born in Dharmapuri, Tamil Nadu, India. He obtained his Doctoral research in the area of alternative fuels at Anna University Chennai, India. He has about 10 years of teaching experience and presently working as an Associate Professor in the Department of Mechatronics Engineering, Hindusthan College of Engineering and Technology(Autonomous), Coimbatore. His areas of interests are alternative fuels, emission control, Heat Transfer.
P. Ganeshan
P. Ganeshan is working as Professor in the Department of Mechanical Engineering in Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India since December 2021. He had completed Bachelor of Engineering in Mechanical Engineering in 2006 and Post Graduate in Engineering Design in 2013 from Anna University, Chennai, Tamil Nadu India. He obtained his Doctorate in Mechanical Engineering, Anna University Chennai in 2017 and has more than 15 years of experience in research, teaching and industry. He has published 85 research article in peer reviewed Journals and participated over 10 National and international conferences. His area of research is the use of the composite materials for Bio-medical applications. Currently he is guiding 08 PhD research scholars in Anna University, Chennai. His Subjects of Interest includes Engineering Mechanics, Fluid Mechanics, Strength of Materials, Theory of Machines and Design of Machine Elements and Transmission systems.
Ahmad A. Ifseisi
Ahmad A. Ifseisi received his B.Sc. degree at the top third and his M.Sc. degree at the top of his classes in 2005 and 2008, respectively, from Hashemite University, Jordan, and received his Ph.D. degree at the top of his class in 2012 from King Saud University, Saudi Arabia. Currently, he is an Assistant Professor of Analytical Chemistry at Department of Chemistry, and Researcher at Advanced Materials Research Chair, King Saud University, Saudi Arabia. He has worked as a researcher at King Abdullah Institute for Nanotechnology (Oct. 2008 – Jan. 2013), researcher assistance at Hashemite University (Mar. 2008 – Aug. 2008), and teaching assistant at Hashemite University (Oct. 2005 – Oct. 2007). He is working on a variety of separation and chromatographic topics ranging from preparation and development of packing materials for chromatographic columns to extraction and preconcentration of various organic and inorganic samples. Ahmed has published more than 80 scientific contributions; 41 original papers, 2 reviews, 3 patents, 3 book chapters, and 42 presentations in local and international conference proceedings. He received the King Saud University Award for scientific excellence, 2014, Almarai company prize for the Best Research Unit, Advanced Materials Research Chair (Team Award), 2014, fellowship for POC2012 IUPAC conference, Qatar Petroleum Company QAPCO, 2012, the vice-rectorate for graduate studies and research prize for the excellence scientific research in the King Saud University, 2011, the distinct and creator students, Hashemite University, 2008, a fully-funded Ph.D. scholarship, King Abdullah Institute for Nanotechnology, King Saud University, and a fully-funded M.Sc. scholarship, Hashemite University.