| 基于SARIMA模型的风电场月发电量预测研究 |
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| 引用本文： | 郭丹,胡博,刘俊德,赵静翔,朴在林. 基于SARIMA模型的风电场月发电量预测研究[J]. 中国电力, 2016, 49(2): 136-141. DOI: 10.11930/j.issn.1004-9649.2016.02.136.05 |
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| 作者姓名： | 郭丹 胡博 刘俊德 赵静翔 朴在林 |
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| 作者单位： | 1.沈阳农业大学 信息与电气工程学院，辽宁 沈阳 110866; 2.国网辽阳供电公司，辽宁 辽阳 111000;3. 中国农业大学 信息与电气工程学院，北京 100083 |
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| 基金项目： | 国家科技支撑计划资助项目（2012BAJ26B01） |
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| 摘 要： | 风力发电由于其清洁性、波动性、随机性及不稳定性，向电网输送绿色电能的同时也对电网的可靠运行造成了一定的冲击，因此风电发电量预测的准确性对电网科学合理调度、安全稳定运行具有至关重要的作用。以大数据分析、多学科交叉融合为背景，以负荷预测为基础理论，利用计量经济学分析方法对风电场月发电量数据进行分析、建模和预测。对辽宁地区某49.5 MW风电场月发电量数据进行收集整理，利用计量经济学分析软件EVIEWS对采样数据进行分析，并采用SARIMA模型对风电场月发电量数据进行拟合和预测，达到了较好的预测效果。
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| 关 键 词： | 风电场 月发电量 SARIMA模型 |
| 收稿时间： | 2015-09-24 |
Research on Monthly Power Generation Forecast of Wind Power Farm Based on Seasonal Auto-Regressive Integrated Moving Average Model |
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| 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. DOI: 10.11930/j.issn.1004-9649.2016.02.136.05 |
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| Authors: | GUO Dan HU Bo LIU Junde ZHAO Jingxiang PIAO Zailin |
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| Affiliation: | 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 |
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| 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. |
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| Keywords: | wind power farm monthly power generation SARIMA model |
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