International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Cost-minimization and Implicit Innovative Training for Energy Prediction using Deep Neural Network

Author(s) S R M TEJESHWAR, B VIJAY GANESH, U G PRADEESHWAR, DEEPIKA
Country India
Abstract To alleviate adverse environmental impacts, power stations and energy grids must optimize resource application for power generation. Accordingly, soothsaying guests' energy consumption has come integral to every energy operation system. exercising data from smart homes, energy operation information can train deep neural networks to anticipate unborn energy demands. As the frugality advances, both energy product and consumption have steadily increased over the times. Amidst global enterprises over energy force and environmental challenges, this study introduces a new vaticination approach using neural networks. By using statistical data from the energy assiduity, these networks directly read changes in energy product and consumption trends. Numerical findings validate the efficacity of this neural network- grounded vaticination system, emphasizing its significance in energy conservation sweats. Predicting energy consumption stands as a pivotal bid in energy conservation enterprise. Support vector retrogression, famed for its efficacity in handlingnon-linear data retrogression challenges, has surfaced as a prominent tool for soothsaying structure energy consumption. Through analysis of literal data, it's apparent that the relationship between lighting energy consumption and its impacting factors is non-linear.
Keywords Environmental impacts; resource optimization; energy consumption trends; power stations; energy grids; smart homes; deep neural networks; global economy; energy production; energy consumption; environmental challenges; prediction methods; statistical data; support vector regression; energy conservation efforts.15
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-16
Cite This Cost-minimization and Implicit Innovative Training for Energy Prediction using Deep Neural Network - S R M TEJESHWAR, B VIJAY GANESH, U G PRADEESHWAR, DEEPIKA - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20137
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.20137
Short DOI https://doi.org/gtvt3f

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