RECURRENT NEURAL NETWORK PREDICTION OF WIND SPEED TIME SERIES BASED ON SEASONAL EXPONENTIAL ADJUSTMENT

Jiang Mingyang; Xu Li; Zhang Kaijun; Ma Yuanxing

doi:10.19912/j.0254-0096.tynxb.2020-0389

PDF(1754 KB)

Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (2) : 444-450. DOI: 10.19912/j.0254-0096.tynxb.2020-0389

RECURRENT NEURAL NETWORK PREDICTION OF WIND SPEED TIME SERIES BASED ON SEASONAL EXPONENTIAL ADJUSTMENT

Jiang Mingyang, Xu Li, Zhang Kaijun, Ma Yuanxing

Author information +

History +

Abstract

A novel method of wind speed prediction based on neural network with seasonal exponential adjustment is proposed. Based on the nonlinear relationships among historical wind speeds and the strong nonlinear fitting ability of neural network, the neural network combined with seasonal exponential adjustment is adopted to predict the wind speed time series. First, time series graph and augmented Dickey-Fuller method are used to test the stability of time series. The results show that time series is unstable. The instability indicates that the time series contains seasonal, trending, cyclic and irregular components. In this paper, this time series decomposition model is used to adjust the seasonal index of time series. Finally, LSTM and GRU neural networks are used to predict wind speed data, and the ideal prediction results are obtained. Compared with the results of raw wind speed data and the seasonal exponential adjustment with the addition model, the results of two neural network methods based on the seasonal exponential adjustment with the multiplication model achieve much higher accuracy of wind speed prediction.

Key words

wind speed prediction / combination forecasting / neural network / long short-term memory network / gated recurrent unit network / time series analysis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Jiang Mingyang, Xu Li, Zhang Kaijun, Ma Yuanxing. RECURRENT NEURAL NETWORK PREDICTION OF WIND SPEED TIME SERIES BASED ON SEASONAL EXPONENTIAL ADJUSTMENT[J]. Acta Energiae Solaris Sinica. 2022, 43(2): 444-450 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0389

References

[1] 杨茂, 杨琼琼, 苏欣. 基于风电场等效平均风速的风电功率日前预测研究[J]. 太阳能学报, 2020, 41(2): 85-92.
YANG M, YANG Q Q, SU X.Ultra-short term probabilistic intervals forecasting of wind power based on optimization model of forecasting error distribution[J]. Acta energy solaris sinica, 2020, 41(2): 85-92.
[2] SOMAN S, ZAREIPOUR H, MALIK O P, et al.A review of wind power and wind speed forecasting methods with different time horizons[C]//2010 North American Power Symposium, Arlington, Texas, USA, 2010.
[3] 张飞民, 王澄海, 谢国辉, 等. 气候变化背景下未来全球陆地风, 光资源的预估[J]. 干旱气象, 2018, 36(5): 725-732.
ZHANG F M, WANG C H, XIE G H, et al.Projection of global wind and solar energy over land under climate change scenarios during 2020-2030[J]. Journal of arid meteorology, 2018, 36(5): 725-732.
[4] KARNAUSKAS K B, LUNDQUIST J K, ZHANG L.Southward shift of the global wind energy resource under high carbon dioxide emissions[J]. Nature geoscience, 2018, 11(1): 38-43.
[5] 罗帅, 丁勤卫, 李春, 等. 风速时间序列混沌特征分析及非线性短期预测[J]. 能源工程, 2019(5): 50-56.
LUO S, DING Q W, LI C, et al.Analysis of chaotic charateristics of wind speed time series and nonlinear short-term prediction[J]. Energy engineering, 2019 (5): 50-56.
[6] 袁全勇, 李春, 杨阳. 风速时间序列非线性特征分析[J]. 热能动力工程, 2018, 33(8): 135-143.
YUAN Q Y, LI C, YANG Y.Nonlinear characteristic analysis of wind speed time series[J]. Journal of engineering for thermal energy and power, 2018, 33(8):135-143.
[7] GUO Z H, WU J, LU H Y.A case study on a hybrid wind speed forecasting method using BP neural network[J]. Knowledge-based systems, 2011, 24(7): 1048-1056.
[8] WU J, WANG J Z, LU H Y.Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model[J]. Energy conversion and management, 2013, 70: 1-9.
[9] ÇEVIK H H, ÇUNKAŞ M, POLAT K, et al.A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods[J]. Physica A: Statistical mechanics and its applications, 2019, 534: 122177.
[10] CHEN J, ZENG G Q, ZHOU W N.Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization[J]. Energy conversion and management, 2018, 165: 681-695.
[11] HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
[12] CHUNG J Y, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. https://arxiv.org/abs//1412.3555. Presented in NIPS 2014 Deep Learning and Representation Learning Work.
[13] WANG J Q, DU Y, WANG J.LSTM based long-term energy consumption prediction with periodicity[J]. Energy, 2020, 197: 117197.
[14] KAREVAN Z, SUYKENS J A K. Transductive LSTM for time-series prediction: An application to weather forecasting[J]. Neural networks, 2020, 125: 1-9.
[15] MUZAFFAR S, AFSHARI A.Short-term load forecasts using LSTM networks[J]. Energy procedia, 2019, 158: 2922-2927.
[16] LIU H, MI X W, LI Y F.Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM[J]. Energy conversion and management, 2018, 159: 54-64.
[17] LIU H, MI X W, LI Y F, et al.Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional gated recurrent unit network and support vector regression[J]. Renewable energy, 2019, 143: 842-854.
[18] DICKEY D A, FULLER W A.Distribution of the estimators for autoregressive time series with a unit root[J]. Journal of the American Statistical Association, 1979, 74(366a): 427-431.
[19] ZHANG G P, QI M.Neural network forecasting for seasonal trend time series[J]. European journal of operational research, 2005, 160(2): 501-514.