A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.