Abstract:Accurate wind speed prediction is the key to large-scale application of wind energy in power system,but the randomness and volatility of wind speed sequence make it difficult to predict.Herein,strategies of Logistic chaotic mapping,adaptive parameter adjustment,and the introduction of mutation are used to improve the Carnivorous Plant Algorithm (CPA),and a short-term wind speed prediction model based on error correction and VMD-ICPA-LSSVM is proposed.First,meteorological factors are used as inputs for Least Squares Support Vector Machine (LSSVM) to predict wind speed and obtain an error sequence.Then,K-L divergence is used to adaptively determine the parameters of Variational Mode Decomposition (VMD) and decompose the error sequence.Then the Improved Carnivorous Plant Algorithm (ICPA) is combined to optimize the adjustable parameters of LSSVM to predict the decomposed subsequences.The prediction results of each subsequence are stacked and error correction is performed on the original prediction sequence to obtain the final wind speed prediction values.The experimental results show that the proposed model has excellent prediction accuracy and generalization performance.

Abstract:Accurate prediction of wind speed in extreme weather can provide important guidance for distribution network to enhance disaster prevention and resilience.This paper proposes a method based on Temporal Convolutional Network (TCN), Bi-directional Long Short-Term Memory(BiLSTM) and error correction for wind speed prediction in extreme weather.First, the time series characteristics of multi-feature weather data are extracted by TCN, and then input into BiLSTM for wind speed prediction.To further improve the prediction accuracy, Variational Mode Decomposition (VMD) is introduced to decompose the error sequence, and BiLSTM models are constructed to perform error prediction for the decomposed error subsequences respectively.Then the error prediction value is used to correct the wind speed prediction value.Finally, simulations are carried out for a place of Henan province, and the results show that compared with measured weather data, the proposed method can effectively predict wind speed with high accuracy when extreme weather occurs.

Abstract:In view of the complicated factors influencing the stock price, we revised the Long Short-Term Memory (LSTM) network, which is commonly used in time series, to predict stock prices under the condition of multivariable.First, the Variance Inflation Factor (VIF) was used to screen variables, and then the adaptive promotion (Adaboost) model was combined to check the importance of characteristic variables.Second, the crawler was used to conduct text analysis of investor sentiment, calculate indicators including sentiment index, and reveal the relationship between them and stock price.Then, prices of three stocks including Gree Electric Appliances, Flyco Electric Appliances and Midea Group were predicted by Multilayer Perceptron (MLP) and LSTM, and the appropriate model was selected as the benchmark model.Finally, indicators of sentiment index and investor concern were added to the benchmark model to construct the LSTM-EM model, and the GM (1, 1) model was used to correct the residual term after considering investor sentiment.The empirical results show that the proposed model can predict the stock price accurately.

Abstract:With the increasing attention focused on the measurement accuracy of the high altitude air temperature,it is necessary to research into the source of the radiosonde temperature sensor error.Solar radiation makes the most significant contribution to the error in upper air temperature measurement.A computational fluid dynamics(CFD) method is employed to study the errors induced by solar radiation and lead angles,from sea level to 32 km altitude.The results show that lead angle and solar incident angle are important factors that affect the errors.Simulation results can be used as a reference for error correcting,which has a potential to improve the measuring accuracy.