Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India.
This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.
Keywords
Smart Grid, Deep Learning, Deep Reinforcement Learning, Renewable Energy, Energy Cost, Energy Storage Management.
T. Kataray et al., “Integration of smart grid with renewable energy sources: Opportunities and challenges – A comprehensive review,” Sustainable Energy Technologies and Assessments, vol. 58, p. 103363, Aug. 2023, doi: 10.1016/j.seta.2023.103363.
H. Elsheikh et al., “Low-cost bilayered structure for improving the performance of solar stills: Performance/cost analysis and water yield prediction using machine learning,” Sustainable Energy Technologies and Assessments, vol. 49, p. 101783, Feb. 2022, doi: 10.1016/j.seta.2021.101783.
S. Durairaj and R. Sridhar, “MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center,” Cluster Computing, vol. 27, no. 2, pp. 1733–1751, Jun. 2023, doi: 10.1007/s10586-023-04040-8.
M. Gandhi et al., “SiO2/TiO2 nanolayer synergistically trigger thermal absorption inflammatory responses materials for performance improvement of stepped basin solar still natural distiller,” Sustainable Energy Technologies and Assessments, vol. 52, p. 101974, Aug. 2022, doi: 10.1016/j.seta.2022.101974.
P. Chithaluru, F. Al-Turjman, T. Stephan, M. Kumar, and L. Mostarda, “Energy-efficient blockchain implementation for Cognitive Wireless Communication Networks (CWCNs),” Energy Reports, vol. 7, pp. 8277–8286, Nov. 2021, doi: 10.1016/j.egyr.2021.07.136.
“Grid Integration of Renewable Energy Sources using GA Technique for Improving Power Quality,” International Journal of Renewable Energy Research, no. v11i3, 2021, doi: 10.20508/ijrer.v11i3.12292.g8283.
Gayathri et al., “Cooperative and feedback based authentic routing protocol for energy efficient IoT systems,” Concurrency and Computation: Practice and Experience, vol. 34, no. 11, Feb. 2022, doi: 10.1002/cpe.6886.
R. C. Bheemana, A. Japa, S. S. Yellampalli, and R. Vaddi, “Negative capacitance FETs for energy efficient and hardware secure logic designs,” Microelectronics Journal, vol. 119, p. 105320, Jan. 2022, doi: 10.1016/j.mejo.2021.105320.
P. Thamizharasu., “Revealing an OSELM based on traversal tree for higher energy adaptive control using an efficient solar box cooker,” Solar Energy, vol. 218, pp. 320–336, Apr. 2021, doi: 10.1016/j.solener.2021.02.043.
T. Vino et al., “Multicluster Analysis and Design of Hybrid Wireless Sensor Networks Using Solar Energy,” International Journal of Photoenergy, vol. 2022, pp. 1–8, Oct. 2022, doi: 10.1155/2022/1164613.
B. Sharma et al., “Designing and implementing a smart transplanting framework using programmable logic controller and photoelectric sensor,” Energy Reports, vol. 8, pp. 430–444, Nov. 2022, doi: 10.1016/j.egyr.2022.07.019.
L. Sathish Kumar et al., “Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–13, Apr. 2022, doi: 10.1155/2022/7901587.
R. Thirumuru, K. Gurugubelli, and A. K. Vuppala, “Novel feature representation using single frequency filtering and nonlinear energy operator for speech emotion recognition,” Digital Signal Processing, vol. 120, p. 103293, Jan. 2022, doi: 10.1016/j.dsp.2021.103293.
N. P. Singh., “Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox theory,” Sustainable Energy Technologies and Assessments, vol. 48, p. 101601, Dec. 2021, doi: 10.1016/j.seta.2021.101601.
L. Maddisetti, R. K. Senapati, and R. JVR, “Accuracy evaluation of a trained neural network by energy efficient approximate 4:2 compressor,” Computers & Electrical Engineering, vol. 92, p. 107137, Jun. 2021, doi: 10.1016/j.compeleceng.2021.107137.
M L., “Deep Learning-Based Smart Hybrid Solar Water Heater Erection Model to Extract Maximum Energy,” International Journal of Photoenergy, vol. 2022, pp. 1–8, Oct. 2022, doi: 10.1155/2022/2943386.
V. Myilsamy, S. Sengan, R. Alroobaea, and M. Alsafyani, “State-of-Health Prediction for Li-ion Batteries for Efficient Battery Management System Using Hybrid Machine Learning Model,” Journal of Electrical Engineering & Technology, vol. 19, no. 1, pp. 585–600, Jun. 2023, doi: 10.1007/s42835-023-01564-2.
V. Arumugham et al., “An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids,” Sustainability, vol. 15, no. 6, p. 5453, Mar. 2023, doi: 10.3390/su15065453.
R. Abdulkader et al., “Soft Computing in Smart Grid with Decentralized Generation and Renewable Energy Storage System Planning,” Energies, vol. 16, no. 6, p. 2655, Mar. 2023, doi: 10.3390/en16062655.
Lal Karn., “An Empirical Analysis of the Effects of Energy Price Shocks for Sustainable Energy on the Macro-Economy of South Asian Countries,” Energies, vol. 16, no. 1, p. 363, Dec. 2022, doi: 10.3390/en16010363.
Lal Karn, P. Selvam Manickam, R. Saravanan, R. Alroobaea, J. Almotiri, and S. Sengan, “IoT Based Smart Framework Monitoring System for Power Station,” Computers, Materials & Continua, vol. 74, no. 3, pp. 6019–6037, 2023, doi: 10.32604/cmc.2023.032791.
Rajagopalan et al., “Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods,” Energies, vol. 15, no. 23, p. 8889, Nov. 2022, doi: 10.3390/en15238889.
S. Sengan, S. V, I. V, P. Velayutham, and L. Ravi, “Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning,” Computers & Electrical Engineering, vol. 93, p. 107211, Jul. 2021, doi: 10.1016/j.compeleceng.2021.107211.
K. S. Kumar, B. P. Esther, V. Indrgandhi, S. Sudhakar, L. Ravi, and V. Subramaniyaswamy, “Area Based Efficient And Flexible Demand Side Management To Reduce Power And Energy Using Evolutionary Algorithms,” Malaysian Journal of Computer Science, pp. 61–77, Nov. 2020, doi: 10.22452/mjcs.sp2020no1.5.
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Raguru Jaya Krishna
Raguru Jaya Krishna
Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India.
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Cite this article
Nabeel S. Alsharafa, Suguna R, Raguru Jaya Krishna, Vijaya Krishna Sonthi, Padmaja S M and Mariaraja P, “Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure", pp. 381-391, April 2024. doi: 10.53759/7669/jmc202404036.
