Contemporary power network planning faces critical challenges from intensifying climate variability,including greenhouse effect amplification,extreme precipitation anomalies,and persistent thermal extremes.These meteo...Contemporary power network planning faces critical challenges from intensifying climate variability,including greenhouse effect amplification,extreme precipitation anomalies,and persistent thermal extremes.These meteorological disruptions compromise the reliability of renewable energy generation forecasts,particularly in photovoltaic(PV)systems.However,current predictive methodologies exhibit notable deficiencies in extreme weather monitoring,systematic transient phenomena analysis,and preemptive operational strategies,especially for cold-wave weather.In order to address these limitations,we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks(TimeGAN)for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data quality verification.In order to better extract the spatiotemporal features of the model input simultaneously,we develop a hybrid neural architecture integrating Convolutional Neural Networks with Long Short-Term Memory modules(CNN-LSTM).To avoid the problem of hyperparameters getting trapped in local optimal solutions,we use the Whale Optimization Algorithm(WOA)algorithm to obtain the global optimal solution by simulating the hunting of humpback whales,further enhancing the generalization ability of the model.Experimental validation demonstrates performance improvements,with the proposed model achieving 30%higher prediction accuracy compared to Genetic Algorithm-Backpropagation Neural Network(GA-BPNN)and Radial Basis Function-Support Vector Regression(RBF-SVR)benchmarks,promoting the renewable energy prediction in data-constrained extreme weather scenarios for future power networks.展开更多
基金supported by Science and Technology Projects of Jiangsu Province(No.BE2022003)Science and Technology Projects of Jiangsu Province(No.BE2022003-5).
文摘Contemporary power network planning faces critical challenges from intensifying climate variability,including greenhouse effect amplification,extreme precipitation anomalies,and persistent thermal extremes.These meteorological disruptions compromise the reliability of renewable energy generation forecasts,particularly in photovoltaic(PV)systems.However,current predictive methodologies exhibit notable deficiencies in extreme weather monitoring,systematic transient phenomena analysis,and preemptive operational strategies,especially for cold-wave weather.In order to address these limitations,we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks(TimeGAN)for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data quality verification.In order to better extract the spatiotemporal features of the model input simultaneously,we develop a hybrid neural architecture integrating Convolutional Neural Networks with Long Short-Term Memory modules(CNN-LSTM).To avoid the problem of hyperparameters getting trapped in local optimal solutions,we use the Whale Optimization Algorithm(WOA)algorithm to obtain the global optimal solution by simulating the hunting of humpback whales,further enhancing the generalization ability of the model.Experimental validation demonstrates performance improvements,with the proposed model achieving 30%higher prediction accuracy compared to Genetic Algorithm-Backpropagation Neural Network(GA-BPNN)and Radial Basis Function-Support Vector Regression(RBF-SVR)benchmarks,promoting the renewable energy prediction in data-constrained extreme weather scenarios for future power networks.
