Enhancing Electrical Power Demand Prediction Using LSTM-Based Deep Learning Models for Local Energy Communities

Abstract

The pursuit of accurate electrical power demand forecasting has led to the application of deep learning algorithms, notably demonstrating promising outcomes despite the prerequisite of substantial training data. This study pioneers a new learning paradigm, employing sophisticated deep learning models specifically, Long Short-Term Memory networks and recurrent neural networks (RNNs). Leveraging historical load data, temperature, wind speed, and day-ahead predicted spot prices, this approach follows a structured flow involving data preprocessing, sequence generation, model training, and subsequent prediction of future load demand using LSTM-based RNN variants. The study’s paramount findings underscore the substantial advancement achieved by this proposed methodology over prevailing techniques. The method significantly improves prediction accuracy by over 11%, demonstrating the efficacy of deep learning models and a significant leap forward in forecasting precision. Beyond its superior predictive capabilities, this novel strategy serves as a catalyst for enhancing energy distribution management in local energy communities. Its effectiveness lies not only in its precision but also in enabling the optimization and cost-effective control of energy distribution, vital for sustainable energy management in these communities. Ultimately, this pioneering approach presents a robust solution poised to revolutionize the landscape of electrical power demand forecasting and its practical application in local energy systems.

Keywords:

• Renewable energy system (RES)
• energy scheduling
• smart grid
• deep learning
• transfer learning
• load demand management

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

M. Pushpavalli

M. Pushpavalli is working as an Assistant Professor in Sathyabama Institute of Science and Technology. She has 17 years of experience in a reputed institution. She completed her B.E (EEE) in Manonamaniam Sundaranar University with First class with distinction. She Secured Sathyabama University Gold Medal in M.E (Power electronics and Industrial Drives) with First Class with Exemplary. She Completed her Doctorate in Sathyabama Institute of Science and Technology. She is certified for 14 NPTEL courses. She has Published nearly 65 papers in various International & national Journals and conferences among that 50 papers indexed in scopus and 12 papers indexed in Web of science. Her Two Patents has been published online. Her Research topics include Power Electronics and Drives, Power Converters, Big data analytics and Machine Learning in smart grids, Artificial Intelligence, Electric Vehicle and Embedded system IoT.

D. Dhanya

D. Dhanya is working as Associate Professor in the Department of Artificial Intelligence & Data Science at Mar Ephraem College of Engineering & Technology, Marthandam. She obtained her Ph.D degree from Anna University, Chennai. With a teaching experience exceeding 10 years, she has published various research papers in esteemed refereed international journals and presented her research at various international conferences. Her contributions to academia have been recognized through multiple best paper awards, which she has received for her exceptional research work at international conferences. She has a diverse background in various areas of study. Her research interests focus on Cloud Computing, Evolutionary Algorithms, Artificial Intelligence, and Wireless Sensor Networks.

Megha Kulkarni

Megha Kulkarni is working as an Associate Professor in the Department of Civil Engineering at Nitte Meenakshi Institute of Technology, Bangalore, Karnataka 560064, India. She obtained her Ph.D degree in Geotech Environmental Engineering,, M. Tech in Environmental Engineering and B.E in Civil Engineering. Her research focuses on infrastructure development in Water supply, Sanitation, Solid waste management, Transportation, Smart cities and Urbanism, Environmental Impact Assessment, Environmental Management, Urban challenges and Agriculture.

R. Rajitha Jasmine

R. Rajitha Jasmine is an Associate Professor in the Department of Information Technology at RMK Engineering College. She obtained her Ph.D degree in Information and Communication Engineering, Anna University, M.E in Computer Science and Engineering from RMK Engineering college (Anna University) and B.Tech (IT) from CSI Institute of Technology (Anna university). Her research area is in Computer Vision and Machine learning. She has 17 Years of working experience in the teaching profession and has handled UG program subjects such as Software Engineering, Computer Architecture, Object Oriented Analysis and Design, Information management. She has presented and published 7 papers in International/National journals. Her area of interest includes Machine Learning, Software Engineering and Computer Networks. She also attended workshops, seminars and faculty development programs. She is a Life member of professional societies like ISTE, ACM and IACSIT.

B. Umarani

B. Umarani is working as Professor in Department of ECE at Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India. She has more than 17 years of Teaching Experience and 5 years of research experience. Her area of specialization includes optical communication networks, Image Processing, Machine Learning and Deep Learning. She is guiding 8 Ph.D Scholars. He has published more than 15 papers in International journals and presented more than 25 papers in National and International Conferences and published 3 books. She has obtained funding projects from various funding agencies.

M. RamprasadReddy

M. RamprasadReddy is working as Associate Professor at Mohan Babu University (MBU), erstwhile Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India. He is distinguished academic with a Ph.D in Electrical and Electronics Engineering from Sri Venkateswara University Tirupati. His expertise encompasses power system analysis operation and control, Flexible AC Transmission Systems and Deregulated power systems, Electric Vehicles and smart Grid. With over 19 years of experience in academia and research he has an impressive portfolio of 10 publications and presented his research at various international conferences. He's deeply involved in mentoring research scholars and engaged in significant projects. Email: [email protected]; Scopus authored: 56035199200

Durga Prasad Garapati

Durga Prasad Garapati is working as Professor in Department of Artificial Intelligence at Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, India. He has more than 15 years of Teaching Experience and 6 years of research experience. His area of specialization includes Machine Learning, Deep Learning and Power Electronics. He has published more than 30 papers in International journals and Conferences. His contributions to research have been recognized through multiple best paper awards, which he has received for his exceptional research work at international conferences.

Ajay Singh Yadav

Ajay Singh Yadav is an accomplished academician with a diverse background. He earned his Ph.D. in two-warehouse inventory models from Banasthali University, Jaipur, Rajasthan, India, in 2011, and later obtained a one-year PDF certificate in Mathematics from Srinivas University, Mangaluru, Karnataka, India, in 2022. Currently serving as an Associate Professor of Mathematics at SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India, he boasts over 17 years of teaching experience at both graduate and postgraduate levels. Yadav has made significant contributions to his field, authoring over 154 research papers published in various national and international journals. Additionally, he holds 30 published patents and 3 granted patents, showcasing his innovative approach. His research is cantered on the development of two-warehouse models using inventory management, with a special emphasis on integrating soft computing and artificial intelligence techniques. In addition to his academic achievements, Dr. Ajay Singh Yadav has also authored a book. His dedication to advancing knowledge and incorporating cutting-edge technologies into inventory management underscores his impactful presence in the academic and research community

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, in 2006, the M.E. degree in Applied Electronics from the Government College of Technology, Anna University, Chennai, India, in 2008 and he received the Full Time Ph.D. degree in Electronics and Communication Engineering from the Anna University of Technology, Coimbatore, India in March 2011. He is currently working as a professor in Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam. 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.