Rui Wang

Wind power curve modeling has important applications in many fields, such as wind turbine condition monitoring and wind power forecasting. There are various studies on wind power curve construction though, the relation between wind speed and errors is not taken into account when constructing wind power curves. To fill this gap, a hybrid copula-based wind power curve model (HCCM) is proposed in this paper. The independent distribution of wind speed is modeled by a Weibull distribution, and the independent distribution of errors is modeled by a mixture of asymmetric gaussian models. Then a hybrid copula model, including four copula functions, is designed to construct the joint distribution. Finally, a grey wolf optimization algorithm is applied to optimize the regression parameters of wind power curves by taking the log-likelihood function of the joint distribution as the fitness function. The proposed model is compared with ten benchmark models on four wind farms and two wind turbines. Experiments show that in terms of RMSE the HCCM achieves 99.3131%, 99.1197%, 99.1715% and 98.9339% on four wind farms, respectively, and around 80% improvement in RMSE on two wind turbines.

Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task. Many studies have been carried out in recent years, but unfortunately, these studies ignore the importance of data preprocessing and the influence of numerous missing values, leading to poor forecasting performance. In this paper, a hybrid forecasting system based on data preprocessing and an Extreme Learning Machine optimized by the cuckoo algorithm is proposed, which can overcome the limitations of the single ELM model. In the system, the standard genetic algorithm is added to reduce the dimensions of the input and utilize the time series model for error correction by focusing on the optimized extreme learning machine model. And according to screened results, the 5% fractile and 95% fractile are applied to compose the upper and lower bounds of the confidence interval, respectively. The assessment results indicate that the hybrid system successfully overcomes some limitations of the single Extreme Learning Machine model and traditional BP and Mycielski models and can be an effective tool compared to traditional forecasting models.

Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series’ forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR) model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.