Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the pur...

To date, exploring an efficient method for optimizing Least Squares Support Vector Machines (LSSVM) hyperparameters has been an enthusiastic research area among academic researchers. LSSVM is a practical machine learning approach that has been broadly utilized in numerous fields. To guarantee its convincing performance, it is crucial to select an appropriate technique in order to obtain the opt...

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational ...

With the improvement of industrialization, importance equipment failure prediction is increasing day by day. Accurate gas-insulated switchgear (GIS) in advance can reduce economic loss caused power system to operate normally. Therefore, a GIS fault approach based on Improved Particle Swarm Optimization Algorithm (IPSO)-least squares support vector machine (LSSVM) proposed this paper. Firstly, f...

This article employs Least Square Support Vector Machine (LSSVM) for determination of Compression Index (Cc) of marine clay in east coast of Korea. This study uses LSSVM as a regression tool. In LSSVM, the regression equation is obtained as the solution to a linear system instead of a quadratic programming (QP) problem. The input parameters of LSSVM are natural water content (n), liquid limit ...

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved o...

Network traffic is a typical time-series data with strong lag and aftereffect, for the existence of local optimum, time-consuming and other defects in the method for the currently determining number of lags, this paper presents a combination of network traffic prediction method (GS-GA-LSSVM). At first, using geo-statistics (GS) to quickly determine the optimal lag order of network traffic, then...

With the rapid development of the photovoltaic industry, fault monitoring is becoming an important issue in maintaining the safe and stable operation of a solar power station. In order to diagnose the fault types of photovoltaic array, a fault diagnosis method that is based on the Least Squares Support Vector Machine (LSSVM) in the Bayesian framework is put forward. First, based on the elaborat...

Forecasting exchange rate requires a model that can capture the non-stationary and non-linearity of the exchange rate data. In this paper, empirical mode decomposition (EMD) is combines with least squares support vector machine (LSSVM) model in order to forecast daily USD/TWD exchange rate. EMD is used to decompose exchange rate data behaviors which are non-linear and nonstationary. LSSVM has b...

Fault diagnosis is a challenging topic for complex industrial systems due to the varying environments such find themselves in. In order improve performance of fault diagnosis, this study designs novel approach by using particle swarm optimization (PSO) with wavelet mutation and least square support (LSSVM). The implementation entails following three steps. Firstly, original signals are decompos...