Deep Learning-Enabled Holistic Control and Prediction System for Building Energy Consumption and Distribution Optimization

Abstract

Managing a building’s energy consumption while minimizing costs and grid reliance is complex. A novel system is proposed, blending advanced technologies and methodologies. Deep learning techniques, particularly the fusion of Hough Transform within a Gated Recurrent Unit, form the core of this approach. User-defined preferences are prioritized, offering personalized energy-saving strategies. Simulation and experimentation demonstrate the system’s efficacy, forecasting energy demand and supply with 4%–10% deviation accuracy in hourly data for months ahead. By leveraging deep learning predictions, efficient energy storage, and an optimized scheduling algorithm, the system achieves an 84% reduction in grid energy reliance, cutting power expenses by 87%. Comparative cost analysis over thirty years highlights the system’s cost-effectiveness and the inefficiency of installing wind turbines on the building’s roof. In essence, this system offers a data-driven solution that optimizes energy management, reduces grid dependency, and tailors energy-saving strategies, significantly cutting electricity expenses.

Keywords:

• energy modeling
• decentralized power generation systems
• deep learning and intelligent structures

Acknowledgement

There is no acknowledgement involved in this work.

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.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

No funding is involved in this work.

Notes on contributors

Dolly Thankachan

Dolly Thankachan is credited with Ph D and M.Tech in ECE from Dr C.V. Raman University and CG State University respectively, completed her bachelor's in Electrical Engineering from IOE Kolkata. She has 08 National and international granted patents and published more than 60 papers in reputed journals, presently she is Associate Professor and Head of Department in Faculty of Electrical and Electronics Engineering, Oriental University, Indore, (M.P.) India.

Senthamizh Selvi Ranganathan

Senthamizh Selvi Ranganathan is an Associate Professor in the department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, Tamil Nadu, India. She obtained her Ph.D degree from Anna University in 2019 and she received Master of Engineering degree in VLSI Design from Anna University in 2007. Her research interests include Speech Processing, Digital Signal Processing, Digital Image Processing and VLSI Design. She has published more than fifty research articles in reputed international journals and conferences. She can be contacted at email: [email protected].

Praba Devi Pachamuthu

Praba Devi Pachamuthu is a professor at Department of Management studies, Sona College of Tecnology and is committed academician with over 25 years of experience. She has worked on projects and published articles in journals with an interest in anchoring training programs for the students, faculty and business community.

Vasanth Ravi

Vasanth Ravi is currently Assistant Professor at School of Computer Science Engineering, Jain University in Bangalore. He completed his degree in B.Tech Information Technology, in 2016 at Anna University, Chennai. He did his M.E Computer Science and Engineering M. Kumarasamy College of Engineering, in 2019 at Anna University, Chennai. He is pursuing his Ph.D in CSE at SRM Institute of Science and Technology, Chennai. His areas of Interests include Deep Learning, Sustainable computing. He can be contacted at email: [email protected].

Geethalakshmi Manickam

Geethalakshmi Manickam received the Engineer degree in Electronics and Communication Engineering from K. S. R. College of Engineering in 2011. She received the Master degree in VLSI Design from K. S. R College of Engineering, Tiruchengode, Tamil Nadu, India in 2013. She is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering at Kongunadu College of Engineering and Technology, Trichy, India. Her area of interests includes VLSI, Image processing, Sensors and Internet of Things. She has published 3 articles in peer reviewed international journals and presented 4 papers in international conferences. She can be contacted at email: [email protected].

Manjunathan Alagarsamy

Manjunathan Alagarsamy received the Engineer degree in Electronics and Communication Engineering from Dr. Navalar Nedunchezhiyan College of Engineering in 2010. He received the Master degree in Embedded System Technologies from Raja College of Engineering and Technology, Madurai, Tamil Nadu, India in 2013. He is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering at K. Ramakrishnan College of Technology, Trichy, India. His area of interests includes Embedded Systems, Image processing, Sensors and Interfacing networks and Internet of Things. He has published 58 articles in peer reviewed International journals and presented 7 papers in International conferences. He can be contacted at email: [email protected].