Abstract
A novel hourly day-ahead power forecasting approach for PV plant based on bidirectional LSTM is proposed in this paper. Firstly, after analyzing the periodic characteristics of PV plant daily power curves, we employ K-means to cluster days into different types of weather according to the irradiance index. Then, a bidirectional Long Short-Term Memory (LSTM) is presented to build forecasting models for each type of weather in four seasons. An empirical study on a real dataset shows that the proposed method can effectively use multivariate time series information to predict the power for PV plants and obtain better performance than Autoregressive Integrated Moving Average model (ARIMA), Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP).
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Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017)
Salcedo-Sanz, S., Casanova-Mateo, C., Pastor-Sánchez, A., Sánchez-Girón, M.: Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization-Extreme Learning Machine approach. Sol. Energy 105, 91–98 (2014)
Wang, F., Mi, Z., Su, S., Zhao, H.: Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 5(5), 1355–1370 (2012)
Leva, S., Dolara, A., Grimaccia, F., Mussetta, M., Ogliari, E.: Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Math. Comput. Simul. 131, 88–100 (2017)
Deo, R.C., Wen, X., Qi, F.: A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl. Energy 168, 568–593 (2016)
Mohammadi, K., Shamshirband, S., Tong, C.W., Arif, M., Petković, D., Ch, S.: A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Convers. Manag. 92, 162–171 (2015)
Li, Y., Su, Y., Shu, L.: An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renew. Energy 66, 78–89 (2014)
Yang, C., Thatte, A.A., Xie, L.: Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation. IEEE Trans. Sustain. Energy 6(1), 104–112 (2015)
Zhu, H., Li, X., Sun, Q., Nie, L., Yao, J., Zhao, G.: A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks. Energies 9(1), 11 (2015)
Sun, H., et al.: Assessing the potential of random forest method for estimating solar radiation using air pollution index. Energy Convers. Manag. 119, 121–129 (2016)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Qing, X., Niu, Y.: Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148, 461–468 (2018)
Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: International Conference on Neural Information Processing Systems, pp, 3104–3112(2014)
Wang, J., Gao, F., Vazquez-Poletti, J., Li, J.: Preface of high performance computing or advanced modeling and simulation of materials. Comput. Phys. Commun. 211 (2017)
Wang, J., Liu, C., Huang, Y.: Auto tuning for new energy dispatch problem: a case study. Future Gener. Comput. Syst. 54, 501–506 (2016)
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The authors would like to thank the Fundamental Research Funds for the Central Universities (2017MS072, 2018ZD06), the National Natural Science Foundation of China (61503137) for financially supporting this work.
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He, H., Hu, R., Zhang, Y., Jiao, R., Zhu, H. (2019). Hourly Day-Ahead Power Forecasting for PV Plant Based on Bidirectional LSTM. In: Hu, C., Yang, W., Jiang, C., Dai, D. (eds) High-Performance Computing Applications in Numerical Simulation and Edge Computing. HPCMS HiDEC 2018 2018. Communications in Computer and Information Science, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-32-9987-0_18
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DOI: https://doi.org/10.1007/978-981-32-9987-0_18
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