Daily Peak Load Forecasting Based on Fast K-medoids Clustering, GARCH Error Correction and SVM Model

doi:10.6180/jase.2016.19.3.02

淡江大學機構典藏 > 淡江出版期刊 > 淡江理工學刊 > 第19卷第3期 > Item 987654321/109061

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题名:	Daily Peak Load Forecasting Based on Fast K-medoids Clustering, GARCH Error Correction and SVM Model
作者:	Song, Zongyun;Niu, Dongxiao;Xiao, Xinli;Wu, Han
关键词:	Daily Peak Load Forecasting;FKM;SVM;GARCH;Error Correction
日期:	2016-09
上传时间:	2017-01-03 09:06:54 (UTC+8)
出版者:	淡江大學出版中心
摘要:	Safe and economic operation of power system is based on load forecasting, and how to increase forecasting accuracy is the premise of power dispatching and economic analysis. Present paper establishes SVM (support vector machine) forecasting model based on fast K-medoids clustering algorithm and data accumulative pre-processing. FKM (fast K-medoids clustering algorithm) is applied to extract similar days by dividing all samples into k clusters, and respective forecasting of k clusters can realize the forecasting of a whole object. Before inputting the data into SVM system, the original data is preprocessed by accumulation to weaken the irregularity disturbance and strengthen sequence regularity. Due to existing unexplained component in forecasting error, GARCH (generalized autoregressive conditional heteroskedasticity) model is employed to forecast the error with non-white noise. According to its results, error correction is applied to the forecasted daily peak load. The forecasting effect of the proposed model is compared with other models in the given example, which verifies that SVM model based on fast K-medoids clustering algorithm and GARCH model has the characteristic of effectiveness, superiority and universality. The accuracy of daily peak load forecasting is enhanced significantly
關聯:	Journal of Applied Science and Engineering 19(3), pp.249-258
DOI:	10.6180/jase.2016.19.3.02
显示于类别:	[淡江理工學刊] 第19卷第3期

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