| 不同时间分辨率的风功率时间序列ARIMA模型预测 |
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| 引用本文： | 张立栋,李继影,吴颖,余侃胜,朱明亮,迟俊宇. 不同时间分辨率的风功率时间序列ARIMA模型预测[J]. 中国电力, 2016, 49(6): 176-180. DOI: 10.11930/j.issn.1004-9649.2016.06.176.05 |
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| 作者姓名： | 张立栋 李继影 吴颖 余侃胜 朱明亮 迟俊宇 |
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| 作者单位： | 1. 东北电力大学 能源与动力工程学院，吉林市 132012; 2. 中广核风电有限公司辽宁分公司，辽宁 沈阳 110000;3. 国网江西省电力公司检修分公司，江西 南昌 330096; 4. 国网江西省电力科学研究院，江西 南昌 330096;5. 吉林省东能电力工程有限公司，吉林 长春 130033; 6. 宾县大个岭风力发电有限公司，黑龙江 哈尔滨 150400 |
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| 摘 要： | 以某风电场同一风力机为研究对象，采用自回归积分滑动平均模型(ARIMA)对5种时间分辨率实际输出功率的时间序列进行预测研究。结果表明:风功率时间序列某些明显的特征点，随着时间分辨率的减小而越来越少直至消失;对预测结果采用平均绝对误差分析，得出随着时间分辨率增大，ARIMA模型预测绝对误差呈现逐渐减小的趋势，1 min的时间分辨率误差最小。
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| 关 键 词： | 时间分辨率 风电场 ARIMA 功率预测 |
| 收稿时间： | 2015-11-26 |
| 修稿时间： | 2016-06-16 |
ARIMA Model Forecast for Wind Power Time Series with Different Temporal Resolutions |
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| ZHANG Lidong,LI Jiying,WU Ying,YU Kansheng,ZHU Mingliang,CHI Junyu. ARIMA Model Forecast for Wind Power Time Series with Different Temporal Resolutions[J]. Electric Power, 2016, 49(6): 176-180. DOI: 10.11930/j.issn.1004-9649.2016.06.176.05 |
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| Authors: | ZHANG Lidong LI Jiying WU Ying YU Kansheng ZHU Mingliang CHI Junyu |
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| Affiliation: | 1. Institute of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, China; 2. CGN Wind Energy Limited Liaoning Branch, Shenyang 110000, China; 3. State Grid Jiangxi Maintenance Company, Nanchang 330096, China;4. State Grid Electric Power Research Institute of Jiangxi Province, Nanchang 330096, China; 5. Jilin East Power Engineering Co., Ltd., Changchun 130033, China; 6. Binxian Dageling Wind Power Generation Co., Ltd., 150400, China |
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| Abstract: | By taking a wind turbine of a farm as a case, the ARIMA model is used to predict the wind power time series according to five time resolutions. The results show that the number of characteristic points of power time series decreases with the reduction of time resolution until the characteristic point disappears completely. The mean absolute error (MAE) is used to analyze the prediction results, and it is concluded that the absolute error of the ARIMA-predicted results decreases gradually with the increase of time resolution with the MAE of 1min resolution being the minimum. |
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| Keywords: | time resolution wind farm ARIMA power prediction |
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