Electric Load Forecasting Based on Locally Weighted Support Vector Regression

Elattar, Ehab E.; Goulermas, John Y.; Wu, Qiuxia

doi:10.1109/tsmcc.2010.2040176

Cited by 238 publications

(105 citation statements)

References 34 publications

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“…Variants of the SVR approach have also been proposed: Jung et al [21] added a genetic algorithm to the least-squares support vector machine (LSSVM), whereas Elattar et al [22] used locally weighted support vector regression. Our local SVR and the approach proposed by Elattar et al are both based on the assumption that the neighbours are the best indicators of the response variable.…”

Section: B Electricity Consumption Predictionmentioning

confidence: 99%

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

Grolinger

Capretz

Seewald³

2016

2016 IEEE International Congress on Big Data (BigData Congress)

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Section: B Electricity Consumption Predictionmentioning

confidence: 99%

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

Grolinger

Capretz

Seewald³

2016

2016 IEEE International Congress on Big Data (BigData Congress)

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“…In other words, the estimated forecast models, whose forecast errors are lower than 1/2, are ignored. Only the high accurate models established in iterative learning process are used to the final forecast according to (8). According to the above derivation, the final forecast accuracy is theoretically guaranteed by the bound-control capability of the boosting algorithm.…”

Section: Forecasting Error Bounds Analysismentioning

confidence: 99%

“…However, the prediction capability of statistical methods drops as the forecast horizon grows. In addition, support vector regression-based methods [7,8] and generalized locally weighted group method of data handling (GMDH) [9] were also proposed in recent years. However, the existing forecasting techniques still cannot adequately meet the engineering requirements.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

Jiang

Chen

et al. 2015

J. Mod. Power Syst. Clean Energy

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Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: Àproposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;`calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.

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“…SVM maps the input space into high-dimensional feature space, constructing linear decision function to replace the nonlinear decision function [14]. Given a sample…”

Section: A Svm Theorymentioning

confidence: 99%

A Short-term Load Forecasting Based on Support Vector Regression

Yu¹

2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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-Load forecasting has become a significant part in national power system strategy management. In this paper, the Support Vector Regression (SVR) for Short-term Load Forecasting (STLF) is presented to predict the primacy of the industry power in the electricity composition. The Support Vector Machine (SVM) is introduced to learn a regression model from training samples with relaxation factors. Our experimental data come from a real-time data acquisition system, which is running for industrial users in a city of Eastern China. As input to the regression model, the feature vector of training samples combines meteorological factors with power system data collected from meters. In order to study the effect of different kernel functions on the accuracy of prediction, this paper respectively tests the linear, polynomial kernel function and Radial Basis Function (RBF). We evaluate the method with two types of predictions, discrete prediction of random samples and continuous prediction of sequential samples. The results indicate that the linear regression model is suitable to forecast with a high fitting degree. However, in the continuous date power prediction, the polynomial kernel function shows preferable prediction ability from the impact of emergencies.

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Electric Load Forecasting Based on Locally Weighted Support Vector Regression

Cited by 238 publications

References 34 publications

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

Energy Consumption Prediction with Big Data: Balancing Prediction Accuracy and Computational Resources

Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

A Short-term Load Forecasting Based on Support Vector Regression

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