Title :
The Short-term Load Forecasting Based on the Rate of Load Fluctuation
Author :
Ma, Rui ; Jiang, Fei ; Song, Junying ; Chen, Huihua ; Dong, Hanbing
Author_Institution :
Changsha Univ. of Sci. & Technol., Changsha, China
Abstract :
The daily load curves of electric power is a kind of nonlinear curve which influenced by residential electricity, weather and some unexpected events. According to the variability of load curves, this paper uses data mining technology and proposes the concepts of Rates of load Fluctuation (RLF) as well as the Impact Factor of the Rate (IFR). Through the analysis of ERCOT data, authors of this paper sum up the characteristics of power consumption in Texas and the similarity of rate changes in the same period of time. Based on statistical analysis of historical data of RLF, it figures out the experience of RLF value. And combined with the real-time load data, it also puts forward the accurate forecasting and correction for short-term load in the application of "combination model". This above mentioned method can be applied to short-term forecasting system in the power system and summarization of the changes of regional mid-long term electricity load.
Keywords :
load forecasting; statistical analysis; ERCOT data; electric power load curves; nonlinear curve; power consumption; power system; rate of load fluctuation; short-term load forecasting system; statistical analysis; Accuracy; Fluctuations; Forecasting; Load forecasting; Load modeling; Predictive models; ERCOT; Load Forecasting; Rates of the LoadFluctuation; the Impact Factor of the Rate; the Statistic Data;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
DOI :
10.1109/ICICTA.2011.247
