Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecastin...

forecasting of municipal waste generation is a critical challenge for decision making and planning,because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (msw) streams analysis and accurate predictions of solid waste quantities generated. due to dynamic and complexity of solid waste management system, models by artificial intell...

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including s...

In order to change the insufficiency of traditional network flow prediction and improve its accuracy, the paper proposed a kind of network flow prediction method based on the self-adaptive genetic least square support vector machine optimization. Through analyzing the individual parameter of the LS-SVM principle and self-adaptive remains algorithm, the network flow prediction model structure of...

Wind speed forecasting can accurately improve prediction efficiency of wind power in wind farm, decrease failure probability of wind turbine, and extend life cycle. An innovative algorithm is proposed to optimize both the parameters of least squares support vector machine (LSSVM) and the procedure of finding sparse support vector. Firstly, the defects of support vector are analyzed. Then inequa...

Accurate and reliable forecasting on energy-related carbon dioxide (CO2) emissions is of great significance for climate policy decision making and energy planning. Due to the complicated nonlinear relationships of CO2 emissions with its driving forces, the accurate forecasting for CO2 emissions is a tedious work, which is an important issue worth studying. In this study, a novel CO2 emissions p...

Langrangian Support Vector Machine (LSVM) and Least Squares Support Vector Machine (LSSVM) are two quick and effective classification methods. In this paper, we first introduce the mathematical models for LSVM and LSSVM and analyze their properties. In the nonlinear case, Sherman-Morrison-Woodbury identity is not used to compute the inversion of a matrix. According to block computation of a mat...

For the limitation of traditional information fusion technology in the mine gas safety class predicition, an intelligent algorithm is proposed in which Genetic Algorithms is adopted to optimize the parameters of the least squares support vector machine and establishes a multi-sensor information fusion model GA-LSSVM which overcomes the subjectivity and blindness on parameters selection, and thu...

The piano key weir (PKW) is a type of nonlinear control structure that can be used to increase unit discharge over linear overflow weir geometries, particularly when the weir footprint area is restricted To predict the outflow passing over a piano key weir, the discharge coefficient in the general equation of weir needs to be known. This paper presents the results of laboratory model testing of...