Title :
Parameters Estimated Subsection Predicting Model for Electric Power Consumption in the Spring Festival
Author :
Chen, Lianghui ; Mai, Jianhao ; Yin, Jian
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China
Abstract :
Because of the Spring Festival effect, it´s hard for traditional methods to predict monthly electricity consumption in January and February. This paper presents a specialized subsection-model which can better reflect the situation to solve this problem. In terms of the holiday condition, the model divides the days of the two months into two parts: the usual electricity days and holiday electricity days. In addition, an algorithm is proposed to estimate the parameters of the subsection-model according to data distribution. The data of similar situation are rare because the date of Spring Festival is different every year, the algorithm overcomes this difficulty. Experiments conducted on real dataset prove the proposed model is effective and accurate.
Keywords :
load forecasting; parameter estimation; power consumption; data distribution; electric power consumption; holiday condition; holiday electricity days; monthly electricity consumption; parameters estimated subsection predicting model; real dataset; spring festival; Accuracy; Electricity; Market research; Prediction algorithms; Predictive models; Smoothing methods; Springs; linear regression; prediction model; prediction of electricity consumption; the Spring Festival;
Conference_Titel :
Computer Science & Service System (CSSS), 2012 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-0721-5
DOI :
10.1109/CSSS.2012.363