首页|Research on Machine Learning Described by Researchers at University of Johannesb urg (Machine Learning Approaches for Power System Parameters Prediction: A Syste matic Review)
Research on Machine Learning Described by Researchers at University of Johannesb urg (Machine Learning Approaches for Power System Parameters Prediction: A Syste matic Review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Johannesburg, South Africa, by NewsRx editors, research stated, "Prediction in the power syste m network is very crucial as expansion is needed in the network."The news editors obtained a quote from the research from University of Johannesb urg: "Several methods have been used to predict the load on a network, from shor t to long time load prediction, to ensure adequate planning for future use. Sinc e the power system network is dynamic, other parameters, such as voltage and fre quency prediction, are necessary for effective planning against contingencies. A lso, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This revie w analyzed different machine learning approaches used for load, frequency, and v oltage prediction in power systems and proposed a machine learning predictive ap proach using network topology behavior as input variables to the model. The anal ysis of the proposed model was tested using a regression model, Decision tree re gressor, and long short-term memory. The analysis results indicate that with net work topology behavior as input to the model, the prediction will be more accura te than when isolated variables of a particular Bus in a network are used for pr ediction."
University of JohannesburgJohannesburgSouth AfricaAfricaCyborgsEmerging TechnologiesMachine Learning
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