Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting , weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. This paper provides a survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources.

Time Series Prediction Using Support Vector Machines: A Survey

A heuristic method for parameter selection in LS-SVM: Application to time series prediction

As fossil fuels reserves deplete rapidly and the low-carbon economy develops expeditiously, especially in China, natural gas as a clean and alternative energy is underway to help meet increased energy needs and climate needs. Therefore, accurate forecasts of natural gas production and consumption have been a necessary task for policy making in the coming years. This paper presents a review of natural gas forecasting models. The multicycle Hubbert model is employed to forecast China’s annual nature gas production and to determine the peak production, the peak year and the future production trends based on several different URR scenarios. Moreover, a small-sample effective rolling GM(1,1) model is proposed for the first time to forecast exponential natural gas consumption with different lengths of data sets. Then, the grey relationship analysis is used to select the best consumption curve in correspond with different URR scenarios. The empirical result shows that the supply–demand gap will be larger and larger in the future, with a minimum gap of 22 bcm in 2011 and 225 bcm in 2050, with a maximum gap of 31 bcm in 2011 and 807 bcm in 2050, which indicates that the natural gas production in China cannot meet the rising consumption. Therefore, policy measures must be taken to ameliorate the situation, including expanding natural gas imports, increasing unconventional natural gas production, complementing the gap with other energy resources and combining energy saving with emission reduction. Accurate forecasting of natural gas production and consumption can provide the basis for decision making and help the government generate new significant policies.

China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model

Accurate heat rate forecasting is very important in ensuring the economic, efficient, and safe operation of a steam turbine unit. The support vector machine (SVM) is a novel tool from the artificial intelligence field that has been successfully applied to heat rate forecasting. The least squares SVM (LS-SVM) is an improved algorithm based on the SVM. LS-SVM has minimal computational complexity and fast calculation. However, traditional LS-SVM, which was established by using offline data samples, can no longer accurately describe the actual system working condition, thereby resulting in problems when directly used in heat rate prediction. In this paper, a heat rate forecasting method based on online LS-SVM, which possesses dynamic prediction functions, is proposed. To avoid blindness and inaccuracy in parameter selection, the gravitational search algorithm (GSA) is used to optimize the regularization parameter @c and the kernel parameter @s^2 of the online LS-SVM modeling. The results confirm the efficiency of the proposed method.

Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

Natural gas consumption forecasting: A discussion on forecasting history and future challenges

Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimal cost. Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique, called support vector machines (SVM), based on the statistical learning theory is explored in this paper for the prediction of natural gas demands. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by the conventional regression techniques. Least squares support vector machines (LS-SVM) is a kind of SVM that has different cost function with respect to standard SVM. The research result shows that the prediction accuracy of SVM is better than that of neural network. The software package NGPSLF based on LS-SVM prediction has been gone into practical business application.

Research on natural gas load forecasting based on least squares support vector machine

Machine learning techniques are finding more and more applications in the field of load forecasting. In this paper a novel regression technique, called Support Vector Machines (SVM), based on the statistical learning theory is explored. SVM is hased on the principle of Structure Risk Minimization as opposed to the principle of Empirical Risk Minimization supported by conventional regression techniques. The natural gas load data in Xi'an city in 2001 and 2002 are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of neural network based model for 7-lead day forecusting. The prediction result shows that prediction accuracy of SVM is better than that of neural network. Thus, SVM appears to he a very promising prediction tool. The software package NGPSLF based on support vector regression (SVR) also has been gone into practical business application.

Gang Zheng

Papers

Research on natural gas load forecasting based on least squares support vector machine

Research on natural gas load forecasting based on support vector regression