Combination Forecasting of Power Load Based on Polynomial Trend Extrapolation and ARIMA Model-Reference-Cited by-同舟云学术

Combination Forecasting of Power Load Based on Polynomial Trend Extrapolation and ARIMA Model

Published:2012-07 Issue: Volume:546-547 Page:357-362
ISSN:1662-8985
Container-title:Advanced Materials Research
language:
Short-container-title:AMR

Author:

Xia Jing Jing¹,Qi Huan¹,Wang Zi Qi²

Affiliation:

1. Huazhong University of Science and Technology

2. Electric Power Dispatching and Communication Center of Henan Province

Abstract

The power load forecasting is the core component of the early warning system for fuel storage margin in power system and an important guarantee to the early warning function to achieve. In this paper, one province's 2008 load data is chosen to forecast the electricity consumption in 2009. Firstly the two forecasting models of polynomial trend extrapolation and ARIMA are established, and then the combined model of them is used to forecast, that is, the final result is equal to the sum of the trend value by polynomial extrapolation and the non-trend D-value’s forecasting result by ARIMA. The results indicate that the combination forecasting make the forecast accuracy significantly improved and ensure the effective operation of the early warning system.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Link

https://www.scientific.net/AMR.546-547.357.pdf

Reference11 articles.

1. Chongqing Kang, Qing Xia, and Mei Liu, Power System Load Forecasting, Beijing: China Electric Power Press, (2007).

2. Hong Xie, Zhonghao Cheng, Guoli Zhang, and Dongxiao Niu, Linear sparse AR forecasting model of load forecasting, Learned journal of North China Electric Power University, Forum Vol. 31, No. 1(Jan. 2004), pp.30-32.

3. Zudi Lu, The compare between correlation structure of double timing AR-MA model and ARMA model, Systems Science and Mathematics, Forum Vol. 15, No. 3(Jul. 1995), pp.222-230.

4. Guijun Ye, Yaohua Luo, Yong Liu, and Hongzhang Jin, Research of load forecasting methods in power system based on ARMA model, Information technology, No. 6(2002), pp.74-76.

5. Liang Chang, Analyzes and forecasts of ARIMA model based on time series analysis, Computer age, No. 2(2011), pp.8-10.

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Peak shaving strategy optimization based on load forecasting: Evidence from Anhui Provence, China;Journal of Energy Storage;2024-06

2. Improved coyote algorithm and application to optimal load forecasting model;Alexandria Engineering Journal;2022-10

3. Study on the Prediction of Consumer Electricity Consumption Based on Hybrid Model;2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2);2021-10-22

4. A Power Load Forecasting Model Based on FA-CSSA-ELM;Mathematical Problems in Engineering;2021-04-23

5. A Novel Combined Model for Short-Term Electric Load Forecasting Based on Whale Optimization Algorithm;Neural Processing Letters;2020-07-20