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    Study Findings from Shiraz University of Technology Broaden Understanding of Cloud Computing (Uncertainty-aware Management of Smart Grids Using Cloud-based Lstm-prediction Interval)

    January 20, 2022 - Information Technology Daily


      2022 JAN 19 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Data detailed on Information Technology - Cloud Computing have been presented. According to news reporting from Shiraz, Iran, by NewsRx journalists, research stated, “This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem.”

      The news correspondents obtained a quote from the research from the Shiraz University of Technology, “A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called ``preparation range” to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods.”

      According to the news reporters, the research concluded: “The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.”

      This research has been peer-reviewed.

      For more information on this research see: Uncertainty-aware Management of Smart Grids Using Cloud-based Lstm-prediction Interval. IEEE Transactions on Cybernetics, 2021:1-14. IEEE Transactions on Cybernetics can be contacted at: Ieee-inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.

      Our news journalists report that additional information may be obtained by contacting Abdollah Kavousi-Fard, Shiraz University of Technology, Dept. of Electronic and Electrical Engineering, Shiraz 715555313, Iran. Additional authors for this research include Seyede Zahra Tajalli, Mohammad Mardaneh, Abbas Khosravi and Roozbeh Razavi-Far.

      The direct object identifier (DOI) for that additional information is: This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

      (Our reports deliver fact-based news of research and discoveries from around the world.)


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