Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization

Abstract

Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the models. Among the models tested, the CNN stack demonstrates the highest precision in estimating energy consumption and solar power output, with a mean absolute error of 0.015% points, a root mean square error of 0.23% points, and an average absolute percentage deviation of 1.71% points. However, for wind turbine (WT) energy generation, the recurrent neural network proves for most accurate model, achieving 0.070 as the median absolute percentage error is 2.65, Both the root of the mean square error and the average absolute error are 0.38. The training and validation data utilized in this study comprise the International Renewable Energy Agency’s data on solar power production, WT power generation, and the “ensue” dataset, which includes hourly power consumption information from the Pennsylvania, New Jersey, and Maryland interconnection.

Keywords:

• demand side power control
• stacked CNN
• deep learning
• renewable energy systems

Disclosure statement

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

Additional information

Notes on contributors

Himanshu Shekhar

Himanshu Shekhar has completed his AMIE (I) in Electronics and Communication Engineering from The Institute of Engineers (India), in 1999. He obtained his M.Tech degree in Digital Electronics from VTU, Karnataka in 2002. He obtained his Ph.D in Software Defined Radio from B.R.A. Bihar University, Muzaffarpur in 2014. He has served in Vellamal Engineering college, Chennai and St Peter’s Engg College, Avadi, Chennai. At present he is Professor in ECE Dept in Hindustan Institute of Technology and science, Padur, Chennai since 2007 till date. He has published more than 29 indexed papers in International/National Journal/Conferences. His research interests include Software Defined radio, Communication systems and VLSI.

Chandra Bhushan Mahato

Chandra Bhushan Mahato, received his B. Sc. Engineering and M.Tech from MIT Muzaffarpur, in 1987 and 1991 respectively. He has obtained his Ph.D degree from BRA Bihar University in 2004. He has joined MIT Muzaffarpur as an Assistant Professor in Jan 1993. In August 2008, he has joined as a Principal in NEC Chandi. Currently he is Principal at MIT Muzaffarpur since July 2021. His main focus of is to empower students with sound knowledge, acumen, experience and training both at the academic level of Engineering and in the cutting edge of global market.

Sanjay Kumar Suman

Sanjay Kumar Suman, received his Ph.D (ICE) and M.E. (ESCS) from MIT Campus, Anna University Chennai and NIT Rourkela, respectively. He has 28 years of experience in teaching, research and industry. Currently he is Dean R&D of St. Martin’s Engineering College, Secunderabad, Telangana, India. He holds a credit of 100 plus publications including Books, Patents, Journals and Conference Proceedings. He is a part of the Reviewer Board for the many Journals like KSII-TIIS, IEEE Access, IET, IETE and Wireless Network. His areas of specialization are Wireless Ad hoc and Sensor Networks, Cognitive Radio, Wireless Communications, Signal Processing, Machine learning application is healthcare and renewable energy.

Satyanand Singh

Satyanand Singh have earned his M.E. and Ph.D. degrees in Electronics & Communication Engineering from NIT Rourkela and Jawaharlal Nehru Technological University, Hyderabad (India). He has two years of post-doctoral research experience with the University of South Pacific Fiji. Presently, he is working with Fiji National University, Fiji, College of Engineering, Science, & Technology, as an associate professor in the School of Electrical & Electronics Engineering. Dr. Singh is a fellow member and chartered engineer of the Institution of Engineers India. Recently, he received a professional membership from the Fiji Institution of Engineers. His primary research interests include speaker recognition, robust speech modeling, feature extraction, pattern recognition, biometrics, and 5G antenna design.

L. Bhagyalakshmi

L. Bhagyalakshmi, received her Ph.D and M.E. from the university campus, Anna University Chennai in the faculty of ICE and Electronics Engineering respectively. After working at Electronics industry for four years, she joined the teaching profession in 2003. Currently she is Professor and Head for the Dept. of ECE, Rajalakshmi Engineering College, Chennai, TN, India. She holds a credit of 100 plus publications including Books, Patents, Journals and Conference Proceedings. She is a part of the Reviewer Board for the Journals like IEEE Access, IET, IETE and Wireless Network. Her areas of specialization are Cognitive Radio, Wireless Communications, Sensor Networks and Signal Processing. Her research work is in Wireless Sensor Networks, application of mobile based technology in agriculture and health sector, 5G Networks and renewable Energy.

Mahendra Prasad Sharma

Mahendra Prasad Sharma received his PhD. Degree in Computer Science & Engineering, M.Tech (CSE) and B.TECH (Information Technology) degree from Institute of Engineering and Technology, Uttar Pradesh Technical University, Lucknow. He has more than 18 years of teaching experience. He has authored many papers in International and national journals/conferences in the area of Deep learning, Pattern Recognition, Network Security, Cryptography and Network Security, Wireless Communication, Ad-Hoc Networking etc. He has more than 22 publications in Indexed journals and conferences, 05 Published Patents, 01 UK Grant Patent, 03 Published Books. Currently, he is Professor and Head of the Department Information Technology, IIMT College of Engineering, Greater Noida.

B. Laxmi Kantha

B. Laxmi Kantha completed her B.Tech (Computer Science and Engineering) from the Gurunanak Engineering College, JNTUH, Hyderabad, Telangana. M.Tech (Computer Science and Engineering) from Malla Reddy Group of Institutions, JNTUH, Hyderabad, Telangana and Ph.D from OPJS University, Churu, Rajasthan. Currently, she is working as Associate Professor with St. Martin’s Engineering College, Dullapally, Secunderabad, Telangana. She has 8 years of teaching experience. She has published 5 research papers in both national and international journals with IEEE conference.

Helan Vidhya T

Helan Vidhya T, is AP of Dept. of ECE, Rajalakshmi Engineering College, Chennai, India. She is having 12 years of teaching experience. She is a member of ISTE and IACSIT, SAEINDIA. She completed B.E (Electronics and communication engineering) in Bhajarang Engineering College, India in 2009 and M.E (Applied Electronics) in St. Joseph's college of Engineering, India in 2011. Her areas of interests are communication engineering, image processing and Deep Learning.

Siva Kumar Agraharam

Siva Kumar Agraharam working as an Associate professor in Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Andhra Pradesh, India. He is having 12 years of teaching experience.

A. Rajaram

A. Rajaram received the B.E. degree in Electronics and Communication Engineering from the Government College of Technology, Coimbatore, Anna University, Chennai, India. The M.E. degree in Electronics and Communication Engineering (Applied Electronics) from the Government College of Technology, Anna University, Chennai, India, and he received the Full Time Ph.D. degree in Electronics and Communication Engineering from the Anna University of Technology, Coimbatore, India. He is currently working as a Professor, ECE Department in E.G.S Pillay Engineering College, Nagapattinam, Tamil Nadu 611002 India. His research interests include Mobile Ad Hoc networks, wireless communication networks (WiFi, WiMax HighSlot GSM), novel VLSI NOC Design approaches to address issues such as low-power, cross-talk, hardware acceleration, Design issues includes OFDM MIMO and noise Suppression in MAI Systems, ASIC design, Control systems, Fuzzy logic and Networks, AI, Sensor Networks, Medical image processing.