Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting
- 26 July 2010
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Power Systems
- Vol. 26 (2), 500-507
- https://doi.org/10.1109/tpwrs.2010.2052638
Abstract
Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China.Keywords
This publication has 19 references indexed in Scilit:
- Short-Term Load Forecasting Using Support Vector Machine with SCE-UA AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Hybrid Load Forecasting Method With Analysis of Temperature SensitivitiesIEEE Transactions on Power Systems, 2006
- Short-Term Load Forecasting Based on an Adaptive Hybrid MethodIEEE Transactions on Power Systems, 2006
- Short-Term Load Forecasting for the Holidays Using Fuzzy Linear Regression MethodIEEE Transactions on Power Systems, 2005
- Next Day Load Curve Forecasting Using Hybrid Correction MethodIEEE Transactions on Power Systems, 2005
- Short-term load forecasting based on support vector machines regressionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001IEEE Transactions on Power Systems, 2004
- Short-term load forecasting via ARMA model identification including non-gaussian process considerationsIEEE Transactions on Power Systems, 2003
- An Introduction to Support Vector Machines and Other Kernel-based Learning MethodsPublished by Cambridge University Press (CUP) ,2000
- Neural network based short term load forecastingIEEE Transactions on Power Systems, 1993