Application of Fuzzy Support Vector Machines in Short-Term Load Forecasting

Li, Yuancheng; Fang, Tingjian

doi:10.1007/3-540-39205-x_58

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…The method is suited to study in little sample. Support vector machine has already become the important research means between the pattern-recognition and the data excavate field at present [7,8].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Unburned Carbon in Fly Ash

Yan¹,

Jun²

2009

EPE

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Numerical simulation of the content of unburned carbon in fly ash on the 300MW tangentially pulverized coal fired boiler is performed by the numerical simulation software COALFIRE, which is based on international advanced TASCFLOW software platform. Firstly, take the result of calculation of number value as the sample, and then set up the support vector machine model of unburned carbon content on the boiler. The relative error between the predicted output and measured value is 0.00186%, which proves the modeling is good for the unburned carbon in fly ash predict.

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Section: Introductionmentioning

confidence: 99%

Modeling of the Unburned Carbon in Fly Ash

Yan¹,

Jun²

2009

EPE

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“…+ b (^'.R"" -^F, coeF (8) b is the threshold value. The problem of the function approximate is equivalent with the minimizing the following problem.…”

Section: Fix)-[^-^(x)]mentioning

confidence: 99%

A Research on Power Load Forecasting Model Based on Data Mining

Sun

Yang

IFIP International Federation for Information Processing

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Abstract. Utilizing the advantage of data mining technology in processing large data and eliminating redundant information, the system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method it can decrease SVM training data and overcome the disadvantage of very large data and slow processing speed when constructing SVM model. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It is denoted that the SVM learning system has advantage when the information preprocessing is based on data mining technology.

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Modeling and Forecasting Electricity Loads and Prices

Weron¹

2006

660

651

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In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components -a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed seasonal volatility technique. In both models the stochastic component is described by an ARMA time series. Models are tested on a time series of system-wide loads from the California power market and compared with the official forecast of the California System Operator (CAISO).

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Application of Fuzzy Support Vector Machines in Short-Term Load Forecasting

Cited by 4 publications

References 2 publications

Modeling of the Unburned Carbon in Fly Ash

Modeling of the Unburned Carbon in Fly Ash

A Research on Power Load Forecasting Model Based on Data Mining

Modeling and Forecasting Electricity Loads and Prices

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