Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instabili...

In recent years, countries have vigorously developed renewable energy resources to alleviate shortages and improve the environment. Wind energy, as a clean new source, has been increasing its power generation capacity, but wind itself characteristics of volatility instability which makes more difficult. Therefore, novel prediction model based on least squares support vector machine (LSSVM) with...

Support Vector Machine(SVM) is a powerful classifier used successfully in many pattern recognition problems. Furthermore, the good performance of SVM classifier has been shown in handwriting recognition field. Least Squares SVM, like SVM, is based on the marginmaximization principle performing structural risk, but its training is easier: it is only needed to solve a convex linear problem rather...

Diagnostics of incipient faults for analog circuits is very important, yet very difficult. Traditionally, the soft faults can only be diagnosed under the occurrence of large variation of component parameters. In this paper, a novel approach for diagnosing incipient faults in analog circuits is proposed. First, a statistical property feature vector composed of range, mean, standard deviation, sk...

The deformation of landslides is a non-linear dynamic and complex process due to the impacts both inherent external factors. Understanding basis landslide essential prevent damage properties losses life. To forecast displacement, hybrid machine learning model proposed, in which Variational Modal Decomposition (VMD) implemented decompose measured total surface displacement into trend periodic co...