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research-article

Inception-embedded attention memory fully-connected network for short-term wind power prediction

Authors:

Mingshan ZhaoAuthors Info & Claims

Volume 141, Issue C

https://doi.org/10.1016/j.asoc.2023.110279

Published: 01 July 2023 Publication History

Abstract

With the increasing demand for energy in the world today, wind energy has turned out to be an attractive alternative to traditional fossil energy sources because of the characteristics of being clean, non-polluting, and easily accessible. Reliably predicting wind power is vital to improving energy utilization and ensuring the stability of power system operation. However, because of the uncertainty and instability of wind energy, accurately predicting wind power is still challenging. Therefore, this study proposes an Inception-embedded attention memory fully-connected network short-term wind power prediction model, incorporating improved attention mechanisms. As a result, the Inception-embedded attention memory fully-connected network can give reliable wind power predictions. This study utilizes a dataset of about 400 days from Natal and compares the Inception-embedded attention memory fully-connected network with 23 algorithms including EffiientNet, NasNet, and ResNet. The comparison results show that the Inception-embedded attention memory fully-connected network obtains reliable wind power prediction one day ahead and outperforms all other compared algorithms by more than 40% in all evaluation metrics.

Highlights

•

The problem of short-term wind power prediction is considered.

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An improvement of the attention mechanism is made and applied to the prediction task.

•

An Inception-embedded attention memory fully-connected network is proposed.

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Forming CNNs, RNNs, and an improved attention mechanism into a combination.

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Optimal prediction accuracy and evaluation metrics are obtained by proposed model.

References

[1]

Nafil A., Bouzi M., Anoune K., Ettalabi N., Comparative study of forecasting methods for energy demand in Morocco, Energy Rep. 6 (6) (2020) 523–536,.

[2]

Weisser D., A wind energy analysis of Grenada: an estimation using the “Weibull” density function, Renew. Energy 28 (11) (2003) 1803–1812,.

[3]

Wang Y., Hu Q., Li L., Foley A.M., Srinivasan D., Approaches to wind power curve modeling: A review and discussion, Renew. Sustain. Energy Rev. 116 (2019),.

[4]

Hänsel M.C., Drupp M.A., Johansson D.J.A., Nesje F., Azar C., Freeman M.C., Groom B., Sterner T., Climate economics support for the UN climate targets, Nature Clim. Change 10 (2020) 781–789,.

[5]

Jennings N., Rao M., Towards a carbon neutral NHS, BMJ 371 (2020) m3884,.

[6]

Zhi M., Fan R., Yang X., Zheng L., Yue S., Liu Q., He Y., Recent research progress on phase change materials for thermal management of lithium-ion batteries, J. Energy Storage 45 (2022),.

[7]

Ullah Z., Elkadeem M.R., Kotb K.M., Taha I.B.M., Wang S., Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply, Renew. Energy 179 (2021) 885–910,.

[8]

Wang Y., Yu X., Yin M., Wang J., Gao Q., Yu Y., Cheng T., Wang Z.L., Gravity triboelectric nanogenerator for the steady harvesting of natural wind energy, Nano Energy 82 (2021),.

[9]

Wang Y., Hu Q., Srinivasan D., Wang Z., Wind power curve modeling and wind power forecasting with inconsistent data, IEEE Trans. Sustain. Energy 10 (1) (2019) 16–25,.

[10]

Chi Y., Xu Y., Ding T., Coordinated VAR planning for voltage stability enhancement of a wind-energy power system considering multiple resilience indices, IEEE Trans. Sustain. Energy 11 (4) (2020) 2367–2379,.

[11]

Liu C., Zhang X., Mei S., Zhen Z., Jia M., Li Z., Tang H., Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness, Appl. Energy 313 (2022),.

[12]

Xu W., Liu P., Cheng L., Zhou Y., Xia Q., Gong Y., Liu Y., Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy, Renew. Energy 163 (2021) 772–782,.

[13]

Yunus K., Thiringer T., Chen P., ARIMA-based frequency-decomposed modeling of wind speed time series, IEEE Trans. Power Syst. 31 (4) (2015) 2546–2556,.

[14]

Wang Y., Zou R., Liu F., Zhang L., Liu Q., A review of wind speed and wind power forecasting with deep neural networks, Appl. Energy 304 (2021),.

[15]

Liu H., Chen C., Data processing strategies in wind energy forecasting models and applications: A comprehensive review, Appl. Energy 249 (2019) 392–408,.

[16]

Carta A., Sperduti A., Bacciu D., Encoding-based memory for recurrent neural networks, Neurocomputing 456 (2021) 407–420,.

Digital Library

[17]

Dai Y., Jin T., Song Y., Sun S., Wu C., Convolutional neural network with spatial-variant convolution kernel, Remote Sens. 12 (17) (2020) 2811,.

[18]

Espejo-Garcia B., Malounas I., Mylonas N., Kasimati A., Fountas S., Using EfficientNet and transfer learning for image-based diagnosis of nutrient deficiencies, Comput. Electron. Agric. 196 (2022),.

Digital Library

[19]

Zoph B., Vasudevan V., Shlens J., Le Q.V., Learning transferable architectures for scalable image recognition, in: arXiv - CS -Computer Vision and Pattern Recognition, (21–26 July). Honolulu, Hawaii, USA, 2017, arXiv:1707.07012 [cs.CV].

[20]

Liu Z., Soft-shell shrimp recognition based on an improved AlexNet for quality evaluations, J. Food Eng. 266 (2020),.

[21]

Ran H., Wen S., Shi K., Huang T., Stable and compact design of memristive GoogLeNet neural network, Neurocomputing 441 (2021) 52–63,.

[22]

Zhu H., Sun M., Fu H., Du N., Zhang J., Training a seismogram discriminator based on ResNet, IEEE Trans. Geosci. Remote Sens. 59 (8) (2021) 7076–7085,.

[23]

Feng J., Li X., Liu X., Chen C., Multigranularity feature fusion convolutional neural network for seismic data denoising, IEEE Trans. Geosci. Remote Sens. 60 (2022) 1–11,.

[24]

Greff K., Srivastava R.K., Koutník J., Steunebrink B.R., Schmidhuber J., LSTM: A search space odyssey, IEEE Trans. Neural Netw. Learn. Syst. 28 (10) (2017) 2222–2232,.

[25]

Javed A.R., Rehman S.u., Khan M.U., Alazab M., T. R. G., CANintelliIDS: Detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU, IEEE Trans. Netw. Sci. Eng. 8 (2) (2021) 1456–1466,.

[26]

Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., Going deeper with convolutions, in: arXiv - CS - Computer Vision and Pattern Recognition, (23–28 June). Columbus, Ohio, USA, 2014, arxiv-1409.4842 [cs.CV].

[27]

Zhou E., Xu X., Xu B., Wu H., An enhancement model based on dense atrous and inception convolution for image semantic segmentation, Appl. Intell. (2022),.

Digital Library

[28]

Ding D., Li J., Zhang K., et al., Non-intrusive load monitoring method with inception structured CNN, Appl. Intell. 52 (2021) 6227–6244,.

Digital Library

[29]

Rodríguez P., Velazquez D., Cucurull G., Gonfaus J.M., Roca F.X., Gonzàlez J., Pay attention to the activations: A modular attention mechanism for fine-grained image recognition, IEEE Trans. Multimed. 22 (2) (2020) 502–514,.

Digital Library

[30]

Girihagama L., Khaliq M.N., Lamontagne P., Perdikaris J., Roy R., Sushama L., Elshorbagy A., Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism, Neural Comput. Appl. (2022),.

Digital Library

[31]

DeRose J.F., Wang J., Berger M., Attention flows: Analyzing and comparing attention mechanisms in language models, IEEE Trans. Vis. Comput. Graphics 27 (2) (2021) 1160–1170,.

Digital Library

[32]

Yan X., Hu S., Mao Y., Ye Y., Yu H., Deep multi-view learning methods: A review, Neurocomputing 448 (2021) 106–129,.

[33]

Silva D.P., Salles J.L.F., Fardin J.F., Pereira M.M.R., Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data, Appl. Energy 278 (2020),.

[34]

Zhang J., Yan J., Infield D., Liu Y., Lien F., Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model, Appl. Energy 241 (2019) 229–244,.

[35]

Yu C., Li Y., Chen Q., Lai X., Zhao L., Matrix-based wavelet transformation embedded in recurrent neural networks for wind speed prediction, Appl. Energy 324 (2022),.

[36]

Yin L., Xie J., Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems, Appl. Energy 283 (2021),.

[37]

Guo Z., Zhao W., Lu H., Wang J., Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model, Renew. Energy 37 (1) (2012) 0960–1481,.

[38]

Bai Y., Liu M., Ding L., Ma Y., Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition, Appl. Energy 301 (2021),.

[39]

Szegedy C., Ioffe S., Vanhoucke V., Alemi A., Inception-v4, inception-ResNet and the impact of residual connections on learning, in: arXiv - CS - Computer Vision and Pattern Recognition, (27–30 June). Las Vegas, Nevada, USA, 2016, arXiv:1602.07261 [cs.CV].

[40]

Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z., Rethinking the inception architecture for computer vision, in: arXiv - CS - Computer Vision and Pattern Recognition, (7–12 June). Boston, Massachusetts, USA, 2015, arXiv:1512.00567 [cs.CV].

[41]

Zhang M., Su H., Wen J., Classification of flower image based on attention mechanism and multi-loss attention network, Comput. Commun. 179 (2021) 307–317,.

Digital Library

[42]

Niu Z., Zhong G., Yu H., A review on the attention mechanism of deep learning, Neurocomputing 452 (2021) 48–62,.

[43]

Shi X., Chen Z., Wang H., Yeung D., Wong W., Woo W., Convolutional LSTM network: A machine learning approach for precipitation nowcasting, in: arXiv - CS - Computer Vision and Pattern Recognition, (7–12 June). Boston, Massachusetts, USA, 2015, arxiv-1506.04214 [cs.CV].

[44]

Choe D., Kim H., Kim M., Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades, Renew. Energy 174 (2021) 218–235,.

[45]

Chung J., Gulcehre C., Cho K., Bengio Y., Empirical evaluation of gated recurrent neural networks on sequence modeling, in: arXiv - CS - Machine Learning, (23–28 June). Columbus, Ohio, USA, 2014, arxiv-1412.3555 [cs.CV].

[46]

Alfke D., Potts D., Stoll M., Volkmer T., NFFT meets Krylov methods: Fast matrix–vector products for the graph Laplacian of fully-connected networks, Front. Appl. Math. Stat. (2018),.

[47]

Li Y., Wang N., Shi J., Hou X., Liu J., Adaptive batch normalization for practical domain adaptation, Pattern Recognit. 80 (2018) 109–117,.

[48]

Eckle K., Schmidt-Hieber J., A comparison of deep networks with ReLU activation function and linear spline-type methods, Neural Netw. 110 (2019) 232–242,.

[49]

Silva D.P., Salles J.L.F., Fardin J.F., Pereira M.M.R., Ottz V.C., da Silva F.B.B., Pignaton E.G., Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid, Data Brief 39 (2021),.

[50]

Yin L., Cao X., Liu D., Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting, Appl. Energy 332 (2023),.

Cited By

Zhao YLiao HPan SZhao Y(2024)Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecastingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124766255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124766
Yin LMo N(2024)Fractional-order Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart gridsApplied Soft Computing10.1016/j.asoc.2024.111825162:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111825

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 141, Issue C

Jul 2023

395 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2023

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Cited By

Zhao YLiao HPan SZhao Y(2024)Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecastingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124766255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124766
Yin LMo N(2024)Fractional-order Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart gridsApplied Soft Computing10.1016/j.asoc.2024.111825162:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111825

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