This paper proposes an innovative framework for medium-term wind power forecasting,employing a robust,multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons...This paper proposes an innovative framework for medium-term wind power forecasting,employing a robust,multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons.The framework consists of three key components:an internal–external learning process,a vertical–horizontal learning process,and a residual-based robust forecasting method.The internal–external process combines Variational Mode Decomposition with a stacked N-BEATS model,achieving stable and accurate forecasts across nearly 200 time steps.The vertical–horizontal process integrates the Polar Lights Optimizer with Joint Opposite Selection and a regression model based on the bidirectional long short-term memory and the gated recurrent unit,enabling efficient hyperparameter optimization and yielding a determination coefficient above 0.9996 for training data and a normalized root mean square error of 0.2448 for test data.We compared our proposed method with nine classical and state-of-the-art techniques and found that it delivers higher accuracy in medium-term prediction,extending to nearly 200 steps.The residual-based method addresses uncertainties by generating 95%confidence intervals,enhancing the model’s robustness in practical applications.By simulating real-world conditions,this framework provides reliable medium-term forecasts,making it an effective tool for renewable energy system dispatch and precise error control.展开更多
基金supported by the Science and Technology Foundation of State Grid Corporation of China 5108-202319067A-1-1-ZN.
文摘This paper proposes an innovative framework for medium-term wind power forecasting,employing a robust,multi-module Artificial Intelligence approach to improve prediction accuracy and reliability over extended horizons.The framework consists of three key components:an internal–external learning process,a vertical–horizontal learning process,and a residual-based robust forecasting method.The internal–external process combines Variational Mode Decomposition with a stacked N-BEATS model,achieving stable and accurate forecasts across nearly 200 time steps.The vertical–horizontal process integrates the Polar Lights Optimizer with Joint Opposite Selection and a regression model based on the bidirectional long short-term memory and the gated recurrent unit,enabling efficient hyperparameter optimization and yielding a determination coefficient above 0.9996 for training data and a normalized root mean square error of 0.2448 for test data.We compared our proposed method with nine classical and state-of-the-art techniques and found that it delivers higher accuracy in medium-term prediction,extending to nearly 200 steps.The residual-based method addresses uncertainties by generating 95%confidence intervals,enhancing the model’s robustness in practical applications.By simulating real-world conditions,this framework provides reliable medium-term forecasts,making it an effective tool for renewable energy system dispatch and precise error control.
