Forecasting the PV Power Utilizing a Combined Convolutional Neural Network and Long Short-Term Memory Model

Abstract

The quantity of photovoltaic systems generated relies on the climate, and to generate significant variations depending on the climatic conditions. Its electricity production processes exhibit unpredictability, instability, and intermittent behavior. The augmented convolutional long-short memory combination network is recommended for PV energy combined forecasting in accordance to overcome the challenge of considerable instability in the high proportion of sustainable energy output. The LSTM and CNN models’ training procedure uses the enhanced artificial bee colony (ABC) to adjust their characteristics. To determine the projected model parameters of PV power, the estimated input parameters of every model are stacked and then reassembled. The studies showed that the forecast precision was significantly higher than the reliability rate of the Convolutional LSTM combinational network as well as the forecasting findings from the BP, CNN, and LSTM independent computational methods. This forecast specificity was predicated on the enhanced ABC to optimize the variables of the CLSTM combinational network for forecasting Photo Voltaic power. For comparative study and assessment, the most recent operational data from a Photovoltaic power plant in northern China have been utilized. The suggested technique resolved more quickly and was higher weather-adaptable than the individual neural network and hybrid forecasting models mentioned prior.

Keywords:

• long short-term memory
• photovoltaic energy forecasting
• CNN
• artificial bee colony
• hybrid CLSTM

Authorship Contributions

All authors are contributed equally to this work

Acknowledgments

The work was supported by Researchers Supporting Project number (RSP2023R492), King Saud University, Riyadh, Saudi Arabia.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

DISCLOSURE STATEMENT

Conflict of Interest is not applicable in this work.

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.

Additional information

Notes on contributors

Ramakrishnan Raman

Ramakrishnan Raman is currently the Director of SIBM, Pune, which is one of the reputed private business Schools in India. He is also the Dean of the Faculty of Management at Symbiosis International (Deemed University) and also holds the position of Director Strategy & Development at Symbiosis International (Deemed University). He has spent majority of his professional career in services sector, ie. Education and Administration in the best business schools in India and abroad. The consulting assignments have helped him spend time in corporate, esp in the area of ERP implementation. He also works with startups in devising strategies to manage their business risks, set up systems and processes to improve their efficiency. His keen inclination towards research has made him publish research papers in refereed International journals.

Bhaveshkumar Mewada

Bhaveshkumar Mewada is an esteemed faculty member working in the Mechanical Engineering Department at Parul Institute of Technology. With a deep passion for mechanical engineering, Dr. Mewada brings a wealth of knowledge and expertise to his role. Dr. Mewada holds advanced academic qualifications in mechanical engineering. As an experienced faculty member, Dr. Mewada is dedicated to inspiring and guiding students in their academic and professional journeys. He combines his strong theoretical foundation with practical insights to deliver engaging lectures and foster a dynamic learning environment. Dr. Mewada actively encourages his students to explore the intersection of mechanical engineering and Computer Science, guiding them towards emerging trends, research opportunities, and industry-relevant projects. He remains up-to-date with the latest advancements in mechanical engineering, continuously expanding his knowledge base to provide students with the most relevant and cutting-edge education. His research interests encompass various aspects of mechanical engineering, with a particular focus on the intersection of mechanical engineering and Information Technology. He has a keen interest in exploring the applications of information technology in the field of mechanical engineering, leveraging technological advancements to enhance efficiency, productivity, and innovation.

R. Meenakshi

R. Meenakshi is a Professor at Chennai Institute of Technology. She completed her M.Tech in Computer Science from SRM University and Ph.D, Image processing from Anna University. She is a research guide at Anna University and teaches subjects related to computer science and engineering. Her research interest includes Big Data Analytics, Image Processing, and Artificial Intelligence.

G. M. Jayaseelan

G. M. Jayaseelan is the Managing Director of Skillssmith. After spending two decades in teaching and training in techno marketing and general management subjects to MBA and Executive MBA students at premier B Schools like SIBM Pune, he has established is own venture to train and deliver Management Development Programs and techno managerial programmes for junior middle and senior level executives of information technology organisations. He has keen interest in marketing, technology and subject related to Artificial Intelligence.

K. Soni Sharmila

K. Soni Sharmila holds a B.Tech degree in Computer Science and Engineering from Shri Vishnu Engineering College for women affiliated to Jawaharlal Nehru Technological University and a M. Tech degree Computer Science and Technology from S.R.K.R. Engineering College Affiliated to Andhra University. In 2012, she joined as an assistant professor in the department of Computer Science and Engineering, WISE engineering college, Prakasaraopalem. She has 10 years of teaching experience in various Engineering Colleges. Currently, she is working as assistant professor in the department of Computer Science and Engineering, Shri Vishnu Engineering College for women, Bhimavaram. Her research work interests in Deep Learning for healthcare informatics.

Syed Noeman Taqui

Syed Noeman Taqui is an Assistant Professor in Bharathi College PG and RC in the discipline of Chemistry and International Graduate Research Assistant perceiving PhD in the Department of Chemistry, Faculty of Science, University of Malaya, Malaysia (Ranked 70 of the top Universities in the world) since 2014 under the supervision of Professor Dr. Rosiyah Yahya. He has worked as Research Assistant in the Department of Mechanical Engineering, Faculty of Engineering, University of Malaya and as Junior Research Fellow at the Department of Chemistry, Jamia Millia Islamia, Central University, New Delhi, India under Indo – Russian project, sponsored by Department of Science and Technology, Government of India during 2013-14. Mr Syed has completed Bachelors and Master degree in distinction with Add-On Proficiency Diploma in Chemistry from University of Mysore during the period 2007-12. Syed has studied and conducted research in varied disciplines and has twenty seven publications to his credit and presented several academic papers in international national conferences and has participated in over 15 workshops, seminars and conferences.

Essam A. Al-Ammar

Essam A. Al-Ammar worked as a Power/Software Engineer at Lucent Technologies in Riyadh for two years. In 2003, he received his MS degree from University of Alabama, Tuscaloosa, AL, and Ph.D. degree from Arizona State University in 2007. In 2021, he obtained MBA from York St. John University, UK, and Post-Graduate Diploma in Digital Business from Emeritus institute of Management in collaboration with MIT Sloan Business School and Columbia Business School. He is a Professor of Electrical Energy in Electrical Engineering Department, King Saud University, Riyadh, Saudi Arabia. He was Governor's Advisor in Electricity Co-Generation Regulatory Authority (ECRA)- now becomes Water and Electricity Regulatory Authority (WERA). Also, he was an advisor at Ministry of Water and Electricity (MOWE) and Energy Consultant for Riyadh Techno Valley (RTV). He is involved in many local and international committees in different aspects in the area of power, electrical energy and water. He is an Opinion Columnist/Writer in Al Eqtisadiah Newspaper, and PMP, RMP and PBA certified from PMI. He is a Senior Member of IEEE since 2007 and Saudi Engineering Committee since 1997. He published nearly 160 papers and 20 patents in Energy, Electricity and Water.

Saikh Mohammad Wabaidur

Saikh Mohammad Wabaidur is a Research associates at the Chemistry Department, King Saud University, Riyadh, Saudi Arabia. He received his MSc degree in organic chemistry in 2004, and PhD degree in 2021 from the Department of Chemistry, Banasthali Vidyapith, Rajasthan, India. He has done some research work from Kyungpook National University, Daegu, South Korea from 2007 to 2009. His research work focuses on the application of carbon nanotubes in electrochemistry, separation of pharmaceutical preparations using HPLC and GC, environmental, chemical and pharmaceutical studies, safe pollutants removal, and synthesis of new silica-based materials for separation (packing materials for chromatographic columns).

Amjad Iqbal

Amjad Iqbal is actively involved in materials engineering and focuses their research on advanced materials designed for high temperatures and their adverse effects. Their main area of interest lies in exploring the characteristics of these materials and studying how they perform under extreme heat conditions. This research is particularly relevant for understanding the behavior of materials in industries such as aerospace, energy, and manufacturing, where withstanding high temperatures is critical. Amjad Iqbal's work may encompass subfields such as high-temperature alloys and ceramic materials, which are engineered to maintain mechanical strength and resist corrosion at elevated temperatures.