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As an emerging clean energy, wind energy has become an important part of energy development all over the world. One of the major ways to use wind energy is wind power. Accurate wind power forecasting is significant to the wind energy development and utilization, and the power systems safe and stable operation. Due to the fluctuation and randomness of wind energy, improving the accuracy of ultra-short-term wind energy prediction has become the key to wind energy development and utilization, and it is also the focus of wind energy development research in various countries. Therefore, this paper proposes a new combination model based on complementary empirical mode decomposition (CEEMD), T-S fuzzy neural network (FNN) optimized by improved genetic algorithm (IGA) and Markov error correction to improve the accuracy of ultra-short-term wind power prediction. First, the CEEMD is used to decompose the wind data into several components; then, the trained IGA-FNN model is used to individually predict each modal component to improve accuracy and stability; finally, the prediction results of all modal components are superimposed and the Markov process is used for error correction to obtain the final prediction result. The empirical results show that the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the proposed model is 15.59%, 17.95% and 6.94%, respectively. The empirical result proves that compared with the BPNN, Elman NN, and FNN, the prediction results MAE of the proposed method is reduced by 68 0.6%, 61.7%, 59.2%, the RMSE is reduced by 70.7%, 65.0%, 63.9%, the MAPE is reduced by 75.5%, 67.6%, 60.4%. The prediction accuracy of the proposed method is significantly higher, and it is available for wind power development and utilization. • A novel wind speed prediction method is proposed, which is beneficial to development of wind energy. • The CEEMD decomposition method is used for decomposition which integrate the advantages of EMD and EEMD algorithms. • The GA method is improved to optimize the FNN, and the combination prediction model is used for wind prediction. • Introducing the Markov chain to correct the prediction error of wind speed. [ABSTRACT FROM AUTHOR]

Canalized rivers are often the product of urbanization. They are an important component of urban blue-green infrastructure whose visual landscape quality affects the aesthetics of a city. Existing research on urban waterways pays little attention to urban canalized rivers and lacks a quantitative interpretation of their visual quality. This study aims to simulate human visual perception and establish quantifiable landscape features for urban riverside greenways. Using a deep learning model to build connections between landscape features and the aesthetic preference of riverside spaces, we establish the best predictive model for the aesthetic quality of urban canalized riverside greenways. We use panoramic photos of rivers in central Beijing and image semantic segmentation technology to extract physical features of landscape composition. We use an ordinary least squares (OLS) regression model and a random forest (RF) model to develop a predictive model for riverside aesthetic quality. The RF model was found to be the most accurate for predicting the aesthetic quality score of the channelized riverside. Trees and bridges are the most important input factors that affect the output score of the RF model. Trees are positively correlated with aesthetic quality, while bridges are negatively correlated. In practice, this model can be used for batch evaluation and aesthetic quality prediction of urban canalized riverside landscapes to help practitioners improve analysis efficiency and guide the management and planning of riverside landscapes. • The riverside greenway landscape of urban canalized rivers is examined. • Quantifiable physical features are used as an index system. • Batch prediction of urban river landscape quality is developed. • Guidance is provided for urban riverside landscape management and planning. [ABSTRACT FROM AUTHOR]