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Summary Traditional power grid investment forecasting models often ignore the cyclical characteristics of historical investment data, leading to one-sided investment allocation results and insufficient model generalization ability. This article proposed a power network investment forecasting method based on particle swarm optimization-gate recurrent unit (PSO-GRU) neural network.
Summary Traditional power grid investment forecasting models often ignore the cyclical characteristics of historical investment data, leading to one-sided investment allocation results and insufficient model generalization ability. This article proposed a power network investment forecasting method based on particle swarm optimization-gate recurrent unit (PSO-GRU) neural network.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
