The short-term load forecasting using the kernel recursive least-squares algorithm

This paper presents a new approach for short-term load forecasting problem based on the kernel recursive least-square algorithm (KRLS). The kernel recursive least-square algorithm is an online real-time kernel-based algorithm and also capable of efficiently solving in recursive manner nonlinear leas...

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Vydáno v:2010 3rd International Conference on Biomedical Engineering and Informatics Ročník 7; s. 2673 - 2676
Hlavní autoři: Chen Liu, Fasheng Liu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2010
Témata:
Approximation algorithms
Artificial neural networks
Forecasting
Kernel
kernel method
Load forecasting
Prediction algorithms
the kernel recursive least-squares algorithm
Time series analysis
ISBN:1424464951, 9781424464951
ISSN:1948-2914
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Shrnutí:This paper presents a new approach for short-term load forecasting problem based on the kernel recursive least-square algorithm (KRLS). The kernel recursive least-square algorithm is an online real-time kernel-based algorithm and also capable of efficiently solving in recursive manner nonlinear least-square predictive problems. In this paper we consider the loads as a time series, through training the KRLS, we give the one-step ahead load forecasting. The test result of short term load forecasting series shows that the precision of load forecasting is greatly improved by means of the new method.
ISBN:1424464951
9781424464951
ISSN:1948-2914
DOI:10.1109/BMEI.2010.5639855