使用快速傅里叶变换优化周期参数的EMD-FFT-SARIMA光伏发电预测模型

熊川羽; 廖晓红; 何诗英; 陈然; 王巍; 臧楠; 王瀛; 肖梦涵

doi:10.11884/HPLPB202436.230349

使用快速傅里叶变换优化周期参数的EMD-FFT-SARIMA光伏发电预测模型

doi: 10.11884/HPLPB202436.230349

熊川羽^1,,
廖晓红¹,
何诗英^2, ,,
陈然¹,
王巍¹,
臧楠^{2, 3},
王瀛^{2, 4},
肖梦涵^{5, 6}

1.
国网湖北省电力有限公司经济技术研究院，武汉 430000
2.
中国科学院合肥物质科学研究院等离子体物理研究所，合肥 230031
3.
安徽建筑大学机械与电气工程学院，合肥 230601
4.
安徽大学物质科学与信息技术研究院，合肥 230601
5.
华中科技大学，电气与电子工程学院，武汉 430074
6.
国网湖北省电力有限公司电力科学研究院，武汉 430000

基金项目: 北省电力有限公司科技研究项目（521538220003）

详细信息

作者简介:
熊川羽，xcy1989528@126.com

通讯作者:
何诗英，shyinghe@ipp.ac.cn。

中图分类号: TM341
计量
- 文章访问数: 342
- HTML全文浏览量: 182
- PDF下载量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-11
- 修回日期: 2024-03-11
- 录用日期: 2024-03-11
- 网络出版日期: 2024-05-27
- 刊出日期: 2024-07-04

EMD-FFT-SARIMA photovoltaic power generation prediction model using fast fourier transform optimization cycle parameters

Xiong Chuanyu^1
,,
Liao Xiaohong¹,
He Shiying^{2
, ,},
Chen Ran¹,
Wang Wei¹,
Zang Nan^{2, 3},
Wang Ying^{2, 4},
Xiao Menghan^{5, 6}

1.
Economic and Technological Research Institute, State Grid Hubei Electric Power Co., Ltd. Wuhan 430000, China
2.
Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031, China
3.
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei 230601, China
4.
Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
5.
School of Electrical Engineering and Electronics, Huazhong University of Science and Technology, Wuhan 430074, China
6.
Electric Power Research Institute, State Grid Hubei Electric Power Co., Ltd. Wuhan 430000, China

摘要

摘要: 根据分布式能源工业园区的光伏电力单元特点，对园区光伏发电功率预测模型进行优化，为后续的调度策略提供数据支持。针对经验模式分解（EMD）与季节性差分自回归移动平均模型（SARIMA）相组合的EMD-SARIMA预测模型中，原始数据经过EMD分解得到的各固有本征模态函数（IMF）分量周期计算问题，提出加入快速傅里叶变换（FFT）的周期计算方法，建立EMD-FFT-SARIMA光伏发电功率预测模型。再将每个IMF对应的预测结果进行叠加重构得到最终的预测结果。通过预测结果的误差计算可以发现，加入FFT环节后均方根误差（RMSE）从120.6 MW下降到19.3 MW，平均绝对误差（MAE）从52.87 MW下降到12.3 MW。
- 经验模式分解 /
- 季节性差分自回归移动平均模型 /
- 周期计算 /
- 固有本征模态函数信号分量 /
- 快速傅里叶变换 /
- 光伏发电预测
Abstract: In this paper, the photovoltaic (PV) power prediction model is optimized according to the characteristics of PV output units in distributed energy industrial parks to provide data support for the subsequent dispatching strategy. The EMD-SARIMA forecasting model is a combination of Empirical Mode Decomposition (EMD) and Seasonal Autoregressive Integrated Moving Average (SARIMA). In the model, the problem of determining the period of each IMF component of the signal component is proposed, the period T calculation method incorporating fast Fourier transform (FFT) is proposed, and the obtained period is fed into SARIMA as an input parameter together with the IMF sequence for prediction, which constitutes the EMD-FFT-SARIMA prediction model. Then, the prediction results corresponding to each IMF are superimposed and reconstructed to obtain the final prediction results. The error calculation of the prediction results reveals that the root mean square error (RMSE) decreases from 120.6 MW to 19.3 MW, and the mean absolute error (MAE) decreases from 52.87 MW to 12.3 MW.
- empirical mode decomposition /
- seasonal autoregressive integrated moving average /
- cycle calculation /
- signal component intrinsic mode function /
- fast Fourier transform /
- photovoltaic power generation forecast

HTML全文

图 1 预测模型流程图

Figure 1. Predictive model flowchart

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图 2 预测值与原始值对比图

Figure 2. Chart of predicted values versus raw values

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图 3 FFT分解后的分量周期和功率图

Figure 3. Component period and power diagram after FFT decomposition

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图 4 IMF-SARIMA 模型的预测误差

Figure 4. Prediction error of the IMF-SARIMA model

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图 5 两个模型预测值的比较

Figure 5. Comparison of the predicted values of the two models

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图 6 EMD-FFT-SARIMA 预测值与原始值的比较

Figure 6. Comparison between EMD-FT-SARIMA predicted values and original values

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表 1 p，q参数选择表

Table 1. p, q parameters selection table

mode	A_CF	P_ACF
AR(p)	trailing	truncated after p-order
MA(q)	truncated after q-order	trailing
ARMA(p，q)	trailing	trailing

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表 2 p，q参数选择

Table 2. p, q parameters selection

p	q	B_AIC
0	1	−1311
1	0	−1369
1	1	−1052

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表 3 P，Q参数选择

Table 3. P, Q parameter selection

P	Q	B_AIC
1	1	−1471
2	1	−1699
3	3	−1162

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表 4 EMD-SARIMA 模型预测误差指标

Table 4. EMD-SARIMA model prediction error indicators

predictive models	δ_RMSE	δ_MAE
EMD-SARIMA	120.6	52.87

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表 5 IMF 部件和周期值

Table 5. IMF components and cycle values

portion size	T_period
IMF1	36
IMF2	24
IMF3	48
IMF4	48
IMF5	48
IMF6	11001
IMF7	97
IMF8	5004
IMF9	13291
IMF10	13078
IMF11	1764
IMF12	3333
IMF13	7500
IMF14	15000
IMF15	15000

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表 6 EMD-FFT-SARIMA的预测结果指标

Table 6. EMD-FFT-SARIMA model prediction error indicators

predictive models	δ_RMSE	δ_MAE
EMD-FFT-SARIMA	19.3	12.3
EMD-SARIMA	120.6	52.87

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