首页|New Machine Learning Data Have Been Reported by Researchers at VIT Bhopal Univer sity (Comparative Study On Forecasting of Schedule Generation In Delhi Region fo r the Resilient Power Grid Using Machine Learning)
New Machine Learning Data Have Been Reported by Researchers at VIT Bhopal Univer sity (Comparative Study On Forecasting of Schedule Generation In Delhi Region fo r the Resilient Power Grid Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating in Sehore, India, by New sRx journalists, research stated, "The increasing use of Renewable Energy Resour ces (RES) in energy generation has led to the transformation of the conventional electrical grid into a more adaptable and interactive system, and this has made electrical load prediction a crucial aspect of smart grid operation. Short-Term Load Forecasting (STLF) is the ultimate requirement for the essentialities, suc h as planning, scheduling, management, and trading of electricity." Financial support for this research came from Regional Meteorological Centre, Ne w Delhi, India. The news reporters obtained a quote from the research from VIT Bhopal University , "In the proposed work, a forecasting engine model is developed to figure out t he load of the upcoming twelve months (2020) in the Delhi metropolis, and this i s accomplished by integrating real and dynamic meteorological data, calendar dat a, and load patterns for the successive two years (2017-2018). It is performed u sing different ensemble models, such as XGBoost, Gradient Boosting, AdaBoost, Ra ndom Forest (RF) algorithms, and deep learning models such as Long Short-Term Me mory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and the Prophet algorithm. The simulation results of the proposed models are obtained o n the Python platform using Delhi weather, load, and calendar data. Further, the STLF is analyzed using 14 different models on the basis of 78 scenarios, and 8 data sets are analyzed in conjunction. The train, validation, and test accuracy have been considered as validation metrics, both on hourly and daily load foreca sting, to validate the overfitting in terms of the train, validation, and test l oss."
SehoreIndiaAsiaCyborgsEmerging T echnologiesMachine LearningVIT Bhopal University
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