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Multi-model fusion short-term load forecasting based on random forest feature selection and hybrid neural network
2021
IEEE Access
In an increasingly open electricity market environment, short-term load forecasting (STLF) can ensure the power grid to operate safely and stably, reduce resource waste, power dispatching, and provide technical support for demand-side response. Recently, with the rapid development of demand side response, accurate load forecasting can better provide demand side incentive for regional load of prosumer groups. Traditional machine learning prediction and time series prediction based on statistics
doi:10.1109/access.2021.3051337
fatcat:xaejvu5xynb45fv4iugakkvgje