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  Published Paper Details:

  Paper Title

Edge-Enabled Load Forecasting In Smart Grids

  Authors

  Shiva Sankari C,  Sneha Priyadharshini V,  Yogitha Vijayakumar,  Rupa Kesavan

  Keywords

Smart grids, Load forecasting, Long Short Term Memory(LSTM), Edge computing

  Abstract


As a result of the smart grid's rapid development, the volume of user-side data has increased dramatically. Load forecasting is vital in order to control the smart grid. But traditional load forecasting methodologies now confront the difficulty of maintaining the accuracy of dynamic forecasting. Due to concerns with latency, security, and other factors, processing all of the data in a centralized data center is not ideal, so we need an effective technique. A potential computing ,edge computing performs calculations locally and overcomes the above challenges. The objective of the project is to meet the requirement of demand analysis using Long Short-Term Memory (LSTM) network based on a short-term load forecasting approach for more accurate load forecasting results.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2305354

  Paper ID - 236021

  Page Number(s) - c693-c706

  Pubished in - Volume 11 | Issue 5 | May 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Shiva Sankari C,  Sneha Priyadharshini V,  Yogitha Vijayakumar,  Rupa Kesavan,   "Edge-Enabled Load Forecasting In Smart Grids", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 5, pp.c693-c706, May 2023, Available at :http://www.ijcrt.org/papers/IJCRT2305354.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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