Solar Energy Forecasting With Performance Optimization Using Machine Learning Techniques

Abstract

The objective of this paper is proposed a forecasting technique system constitute the data integrity phase using Machine Learning (ML) techniques and post-processing output optimization to improve the performance. The outcomes demonstrated that the Photo Voltaic (PV) energy manufacture neural network performed more accurate by reducing solar irradiance prediction errors of linear regression characteristics. Through the adoption of both the optimized method settings and chosen function of ML methods were evaluated for predicting the performance. The accuracy of solar radiation estimation could be assessed in terms of statistical error measurement and verification measurements. The approach was tested in two climates hot and cold and the findings demonstrated using absolute error percentage with a contribution of 6.3% and 4.7%, respectively to improve the performance of the system based on data-driven ML algorithms and analytical post-processing.

Keywords:

• photovoltaic
• solar irradiance
• forecasting
• machine learning
• performance
• accuracy

ETHICAL APPROVAL

No participation of humans takes place in this implementation process.

HUMAN AND ANIMAL RIGHTS

No violation of Human and Animal Rights is involved.

ACKNOWLEDGEMENT

There is no acknowledgement involved in this work.

AUTHORSHIP CONTRIBUTIONS

All authors are contributed equally to this work.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

DATA AVAILABILITY STATEMENT

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

Additional information

Funding

No funding is involved in this work.

Notes on contributors

Murugesan S

Murugesan S received his Ph.D. degree in Information and Communication Engineering from Anna University in 2023. He is currently working as Associate Professor of CSE at Tagore Engineering College, Chennai. She has presented papers in National and International Conferences. He has published many research articles, book chapters in reputed International Journals. His research interests include Data Mining, Deep Learning, Wireless Sensor Networks and Cloud Computing.

M. Mahasree

M. Mahasree affiliated to the Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamilnadu, India, since Jan 2022. She completed M.E. program from Annamalai University, Chidambaram, India (2015–2017) in Computer Science and Engineering and subsequently earned Doctoral Degree from the same University (2018–2022). She has presented papers in National and International Conferences. She also has published articles, book chapters in reputed International Journals. Her research areas of interest include Computer Vision, Data Security and Deep Learning.

F. Kavin

F. Kavin is an Instrumentation Engineer at Muscat Engineering Consultancy, Chennai, India. He obtained a Ph.D. in Electrical engineering from Anna University Chennai, India in 2023. He holds a master’s in Control and Instrumentation from Velammal Engineering College, Surapet , Tamil Nadu, India. He has worked with GE Gas Power Instrumentation as an Instrumentation Engineer. With Over 10 years of Academic and Engineering consultancy experience. He has worked on several projects and guided students in publishing papers at National conference and international conferences. His research area is the Fractional PID controller and MPC controller for the desalination process, Sensor network, and Industrial Data network.

N. Bharathiraja

N. Bharathiraja currently an Associate Professor at Chitkara University, Punjab, India, earned his Ph.D. in Computer Science and Engineering from Anna University Chennai in 2019. Holding a master’s degree in Software Engineering from Anna University Tiruchirappalli (2010), he boasts over 13 years of teaching experience. With a robust academic profile, he has presented 20 papers at National/International conferences and published more than 26 research papers in esteemed international journals. Noteworthy is his collaboration with foreign experts, showcasing a global academic impact. His research interests span Data Structures, Service-Oriented Architecture, Sensor Networks, and Data Sciences with Python. Beyond research, he actively mentors undergraduate students, contributing significantly to the academic and research community. He is a versatile academic, seamlessly integrating teaching, research, and mentorship, making notable strides in the dynamic field of Computer Science and Engineering.