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摘要近年来，随着新能源的快速发展，风力发电在各国的发电构成中所占比例也越来越大。但是，风速的间歇性、波动性也给电网的安全经济运行以及电能质量的保障提高了难度。因此，准确的预测风速，不仅能提高风电行业的市场竞争力；而且为电力部门的安全调度提供参考数据，这对于风电的发展有着十分重要的意义。39875
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本文介绍了风电功率的预测发展现状，并且选择使用时间序列法来建立预测模型。在对时间预测法的分类以及基本原理做出简单介绍后，分别使用自回归求和滑动平均模型（ARIMA）以及季节性自回归差分移动平均模型（SARIMA）对一组实际的风速序列以及风电功率序列进行建模预测。具体过程是先处理数据序列，然后确定模型及阶数，最后建模预测后24小时的数据；对比两种模型的预测结果，在风速预测的算例中ARIMA模型的预测平均相对误差为9.94%，SARIMA模型的预测平均相对误差为8.55%；而在风电功率的预测中，ARIMA模型以及SARIMA模型的预测平均相对误差分别为74.8%和17.3%。
通过本论文中具体案例的分析对比，可以看出对于短期风速以及风电功率的预测，SARIMA模型要比ARIMA模型的预测效果更好。
毕业论文关键词： 风力发电 风速 风电功率 时间序列 预测模型
Abstract
In recent years, with the rapid development of new energy in the world, the Proportion of Wind Power in Power grid is larger and larger in every country. However, the intermittence and randomicity of the wind make it harder to ensure the safety and the qualification of the Power grid. Therefore, it has the extremely vital significance for the development of Wind Power to predict the wind speed accurately .It not only can improve the market competitiveness of wind power but also can provide reference data for the electric power sector.
This paper introduces the developing situation of the wind power prediction and establishes the prediction model by using Time Series method. Then making the prediction model by Autoregressive Intergrated Moving Average(ARIMA) and Spring Autoregressive Intergrated Moving Average(SARIMA) respectively after making a simple introduction to the method and basic principle of Time Series. This paper makes prediction models for a group of the actual wind speed and wind power sequences . firstly , dealing with specific data sequence, then determining the model and the order number, lastly predicting 24 hours dates after determining the specific model. Comparing forecast results of both models, in the wind speed prediction example, ARIMA model to predict the average relative error is 9.94%, SARIMA model to predict the average relative error is 8.55%, and in wind power prediction, ARIMA model and SARIMA model to predict the average relative error of 74.8% and 17.3% respectively.
Through the analysis of specific cases, it can be seen that on the predictions of a short-term wind speed and wind power, SARIMA model is better than the ARIMA model .
Key word：Wind power generation Wind Speed Wind Power Time Series Prediction Models
目录
摘 要 I
Abstract II
1绪论 1
1.1课题研究背景及意义 1
1.2风力发电的发展现状 2
1.2.1世界风力发电发展现状 2
1.2.2国内风力发电发展现状 4
1.3风电功率预测研究发展现状 5
1.4本论文的主要工作 6
2时间序列研究方法 7
2.1时间序列模型概述 7
2.2平稳时间序列模型 7
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