Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information

Donghun Lee, Kwanho Kim


Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

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Ashraf, I. and Chandra, A., “Artificial Neural Network Based Models for Forecasting Electricity Generation of Grid Connected Solar PV Power Plant,” International Journal of Global Energy Issues, Vol. 21, No. 1-2, pp. 119-130, 2004.

Bae, J. K., Lee, S. Y., and Seo, H. J., “Artificial Intelligence Techniques for Predicting Online Peer-to-Peer(P2P) Loan Default,” The Journal of Society for e-Business Studies, Vol. 23, No. 3, pp. 207-224, 2018.

Cha, W. C., Park, J., Cho, U., and Kim, J. C., “Design of Generation Efficiency Fuzzy Prediction Model Using Solar Power Element Data,” The Transactions of The Korean Institute of Electrical Engineers, Vol. 63, No. 10, pp. 1423-1427, 2014.

Chen, C., Duan, S., Cai, T., and Liu, B., “Online 24-h Solar Power Forecasting Based on Weather Type Classification Using Artificial Neural Network,” Solar Energy, Vol. 85, No. 11, pp. 2856-2870, 2011.

da Silva Fonseca Jr, J. G., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y., and Ogimoto, K., “Use of Support Vector Regression and Numerically Predicted Cloudiness to Forecast Power Output of a Photovoltaic Power Plant in Kitakyushu, Japan,” Progress in Photovoltaics: Research and Applications, Vol. 20, No. 7, pp. 874-882, 2012.

Detyniecki, M., Marsala, C., Krishnan, A., and Siegel, M., “Weather-based Solar Energy Prediction,” WCCI 2012 IEEE International conference on Fuzzy Systems, pp 1-7, 2012.

Ding, M., Wang, L., and Bi, R., “An ANN-based Approach for Forecasting The Power Output of Photovoltaic System,” Proceeding of Environmental Sciences, Vol. 11, No. 1, pp. 1308-1315, 2011.

Edward, G., Box, P., and Jenkins, G. M., “Time Series Analysis: Forecasting and Control,” The Journal of Technometrics, Vol. 37, pp. 238-242, 1995.

Fernandez-Jimenez, L. A., Muñoz-Jimenez, A., Falces, A., Mendoza-Villena, M., Garcia-Garrido, E., Lara-Santillan, P. M., Zorzano-Alba, E., and Zorzano-Santamaria, P. J., “Short-term Power Forecasting System for Photovoltaic Plants,” Renewable Energy, Vol. 44, pp. 311-317, 2012.

Hagan, M. T. and Menhaj, M. B., “Training Feedforward Networks with the Marquardt Algorithm,” Proceeding of IEEE Transactions on Neural Networks, Vol. 5, No. 6, pp. 989-993, 1994.

Izgi, E., Öztopal, A., Yerli, B., Kaymak, M. K., and Şahin, A. D., “Short-mid-term Solar Power Prediction by Using Artificial Neural Networks,” Solar Energy, Vol. 86, No. 2, pp. 725-733, 2012.

Inman, R. H., Pedro, H. T., and Coimbra, C. F., “Solar Forecasting Methods for Renewable Energy Integration,” Progress in Energy and Combustion Science, Vol. 39, No. 6, pp. 535-576, 2013.

Jung, H. I., Park, I. S., and Ahn, H., “Identifying the Key Success Factors of Massively Multiplayer Online Role Playing Game Design using Artificial Neural Networks,” The Journal of Society for e-Business Studies, Vol. 17, No. 1, pp. 23-38, 2012.

Kardakos, E. G., Alexiadis, M. C., Vagropoulos, S. I., Simoglou, C. K., Biskas, P. N., and Bakirtzis, A. G., “Application of Time Series and Artificial Neural Network Models in Short-term Forecasting of PV Power Generation,” In Proceedings of the 48th International Universities Power Engineering Conference, pp. 1-6, 2013.

Kim, D. H. and Kim, J. O., “The Solar Power with Weather and Generator Scheduling,” KIEE Summer Conference, p. 131, 2008.

Kou, J., Liu, J., Li, Q., Fang, W., Chen, Z., Liu, L., and Guan, T., “Photovoltaic Power Forecasting Based on Artificial Neural Network and Meteorological Data,” Proceeding of IEEE Region 10 Conference, pp. 1-4, 2013.

Kingma, D. P. and Ba, J., “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.

Lee, I. R., Bae, I. S., Jung, C. H., Kim, J. O., and Shim, H., “Photovoltaic Generation System Output Forecasting Using Irradiance Probability Distribution Function,” KIEE Summer Conference, pp. 548-550, 2004.

Lee, H., “The Development of The Predict Model for Solar Power Generation Based on Current Temperature Data in Restricted Circumstances,” Journal of Digital Contents Society, Vol. 17, No. 3, pp. 157-164, 2016.

Li, Y., He, Y., Su, Y., and Shu, L., “Forecasting The Daily Power Output of a Grid-connected Photovoltaic System Based on Multivariate Adaptive Regression Splines,” Applied Energy, Vol. 180, No. 15, pp. 392-401, 2016.

Li, Y., Su, Y., and Shu, L., “An ARMAX Model for Forecasting the Power Output of a Grid Connected Photovoltaic System,” Renewable Energy, Vol. 66, No. 1, pp. 78-8, 2014.

Li, M., Zhang, T., Chen, Y., and Smola, A. J., “Efficient Mini-batch Training for Stochastic Optimization,” In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 661-670, 2014.

Liu, G., Xu, Y., and Tomsovic, K., “Bidding Strategy for Microgrid in Day-ahead Market Based on Hybrid Stochastic/ Robust Optimization,” IEEE Transactions on Smart Grid, Vol. 7, No. 1, pp. 227-237, 2016.

Pedro, H. T. and Coimbra, C. F., “Assessment of Forecasting Techniques for Solar Power Production with no Exogenous Inputs,” Solar Energy, Vol. 86, No. 7, pp. 2017-2028, 2012.

Shi, J., Lee, W. J., Liu, Y., Yang, Y., and Wang, P., “Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines,” IEEE Transactions on Industry Applications, Vol. 48, No. 3, pp. 1064-1069, 2012.

Sulaiman, S. I., Rahman, T. A., and Musirin, I., “Partial Evolutionary ANN for Output Predictionof a Grid-Connected Photovoltaic System,” International Journal of Computer and Electrical Engineering, Vol. 1, No. 1, pp. 40-45, 2009.

Tao, C., Shanxu, D., and Changsong, C., “Forecasting Power Output for Grid-connected Photovoltaic Power System without Using Solar Radiation Measurement,” In Proceedings of the International Symposium on Power Electronics for Distributed Generation Systems, pp. 773-777, 2010.

Wang, S., Zhang, N., Zhao, Y., and Zhan, J., “Photovoltaic System Power Forecasting Based on Combined Grey Model and BP Neural Network,” In Proceedings of International Conference on Electrical and Control Engineering, pp. 4623-4626, 2011.

Yule, G. U., “Why do We Sometimes Get Nonsense-Correlations Between Time-Series?: A Study in Sampling and the Nature of Time-series,” Journal of the Royal Statistical Society, Vol. 89, No. 1, pp. 1-63, 1926.

Yu, L., Zhou, L., Tan, L., Jiang, H., Wang, Y., Wei, S., and Nie, S., “Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, China,” PloS one, Vol. 9, No. 6, 2014.

Yona, A., Senjyu, T., Funabashi, T., and Kim, C. H., “Determination Method of Insolation Prediction with Fuzzy and Applying Neural Network for Long-term Ahead PV Power Output Correction,” IEEE Transactions on Sustainable Energy, Vol. 4, No. 2, pp. 527-533, 2013.

Zhou, Y., Wang, C., Wu, J., Wang, J., Cheng, M., and Li, G., “Optimal Scheduling of Aggregated Thermostatically Controlled Loads with Renewable Generation in the Intraday Electricity Market,” Applied Energy, Vol. 188, pp. 456-465, 2017.


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