Research on Monthly Power Generation Forecast of Wind Power Farm Based on Seasonal Auto-Regressive Integrated Moving Average Model

doi:10.11930/j.issn.1004-9649.2016.02.136.05

Electric Power ›› 2016, Vol. 49 ›› Issue (2): 136-141.DOI: 10.11930/j.issn.1004-9649.2016.02.136.05

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Research on Monthly Power Generation Forecast of Wind Power Farm Based on Seasonal Auto-Regressive Integrated Moving Average Model

GUO Dan¹, HU Bo¹, LIU Junde², ZHAO Jingxiang³, PIAO Zailin¹

1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China;
2. State Grid Liaoyang Electric Power Supply Company, Liaoyang 111000, China;
3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Received:2015-09-24 Online:2016-02-18 Published:2016-03-21
Supported by:
This work is supported by National Natural Science Foundation of China(No. 71473083) and Beijing Natural Science Foundation (No. 9142016).

Abstract

Abstract: Due to the characteristics of volatility and randomness, wind power brings a certain impact on reliable operation of power grid. Therefore, the accuracy of wind power generation forecast plays a critical role in scientific and reasonable dispatching, and will also influence the safe and stable operation of power grid. Under the background of big data analysis and multidisciplinary integration, the econometric method has been applied in this paper to analyze the monthly power generation data of wind power farms and models are constructed for power generation forecast based on the basic theory of load forecasting. The monthly power generation data of a 49.5 MW wind power farm in Liaoning area are collected and analyzed by using the econometric software EVIEWS, and the SARIMA model is used to forecast the monthly power generation of the wind power farm with a satisfactory results.

Key words: wind power farm, monthly power generation, SARIMA model

CLC Number:

TM715
TK81

GUO Dan, HU Bo, LIU Junde, ZHAO Jingxiang, PIAO Zailin. Research on Monthly Power Generation Forecast of Wind Power Farm Based on Seasonal Auto-Regressive Integrated Moving Average Model[J]. Electric Power, 2016, 49(2): 136-141.

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Research on Monthly Power Generation Forecast of Wind Power Farm Based on Seasonal Auto-Regressive Integrated Moving Average Model

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