Abstract
With the continuous development of intelligent power grid, how to boost the prediction ability of the future operating mode of information equipment and set the dynamic provision for prediction intervals to adapt to the changes of data are huge challenges for the grid IT operation and maintenance. To solve these problems, this paper proposes a combined time series forecasting model (SARIMA-GRU) based on the traditional Seasonal ARIMA model (SARIMA) and the GRU model in Deep learning, which is the error-fitting model by using the error auto-regressive method to compensate for the prediction result. In order to establish the threshold interval in line with the actual production demand, SARIMA-GRU applies the statistical method and K-nearest neighbor algorithm for global preprocessing, and then divides the non-stationary series into three main components of model: trends, seasonality, and residual terms. By using the corresponding model components to predict, we achieve higher prediction accuracy under the normal operation state. On a real-world power grid dataset, we demonstrate more significant performance improvements over the traditional model ARIMA, SARIMA and combination model, like ARIMA-SVM, and showcase three actual threshold intervals.
This work is supported by the National Natural Science Foundation of China (Grant No. 71633006) and Intelligent software and hardware system of medical process assistant and its application belong to “2030 Innovation Megaprojects” (to be fully launched by 2020) - New Generation Artificial Intelligence (Project no. 2020AAA0109605).
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References
Zhengguo, G., Yunsheng, L., Peng, W., Tingting, L., Wei, L.: Research and practice on informatization construction of electric power design enterprises. Eng. Technol. (Citation Version) 10, 211 (2016)
Bagdadee, A.H., Zhang, L.: Smart grid: a brief assessment of the smart grid technologies for modern power system. J. Eng. Technol. 8(1), 122–142 (2019)
Dinda, P.A., O’Hallaron, D.R.: Host load prediction using linear models. Clust. Comput. 3(4), 265–280 (2000)
Zonghua, Z., Haiquan, Z., Chi, W., Xinzheng, N.: Load prediction based on an improved AR model with weighting. Comput. Measur. Control 24(3), 248–251 (2016)
Gang, L., Wenjing, W.: Storage load forecasting research based on time series. Intell. Comput. Appl. 8(3), 188–190 (2018)
Wang, D., Huang, L., Chang, J., Mei, K., Niu, X.: Load forecasting model based on ARIMA and CART. J. Shenzhen Univ. (Sci. Eng.) 36(3), 245–251 (2019)
Bin, Z.B.: A load balancing predication algorithm of CART and KNN. J. Beijing Univ. Posts Telecommun. A1, 93–97 (2017)
Yi, R., Yu, J., Wu, B., Gong, Y.: An improved DV-HOP algorithm based on weighted hyperbolic positioning. Fire Control Command Control 41(12), 96–100 (2016)
Qian, Z.L.: Two improvements on CART decision tree and its application. Comput. Eng. Des. 36(5), 1209–1213 (2015)
Wu, Y., Shen, K., Chen, Z., Wu, J.: Automatic measurement of fetal cavum septum pellucidum from ultrasound images using deep attention network. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2511–2515 (2020). https://doi.org/10.1109/ICIP40778.2020.9191002
Gasparin, A., Lukovic, S., Alippi, C.: Deep learning for time series forecasting: the electric load case (2019)
Choi, H.K.: Stock price correlation coefficient prediction with ARIMA-LSTM hybrid model (2018)
Han, P., Wang, D., Wang, F.: Multi-step-ahead prediction of wind speed time series based on error compensation. Comput. Simul. 31(2), 206–209 (2014)
Goel, H., Melnyk, I., Banerjee, A.: R2N2: residual recurrent neural networks for multivariate time series forecasting (2017)
Pavithra, M., Saruladha, K., Sathyabama, K.: GRU based deep learning model for prognosis prediction of disease progression. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 840–844 (2019). https://doi.org/10.1109/ICCMC.2019.8819830
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, New York (2015)
Lu, J., Du, J., Cao, M., Fan, X.: Carbon emissions trading price prediction using the ARIMA-SVM model. J. Xi’an Univ. Sci. Technol. 40(3), 542–548 (2020)
Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition. J. Off. Stat. 6(1), 3–73 (1990)
Tang, X., Deng, G.: Improvement of ARMA model based on Gevers-Wouters algorithm. Stat. Decis. 2, 19–23 (2018)
Zhu, H., Yuan, Z., Yu, K.: Continuous blood pressure prediction based on the hybrid model of ARIMA and SVM. J. Hangzhou Normal Univ. (Nat. Sci. Ed.) 17(5), 555–560 (2018)
Li, X., Sang, Y., Xie, P., Gu, H.: A method for testing the stationarity of stochastic hydrological process based on discrete wavelet. Syst. Eng. Theory Pract. 38(7), 1897–1904 (2018)
Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654–669 (2018)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 1–12 (2018)
Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017)
Tang, M., Lei, X., Long, Y., Tan, Q., Zhang, Z.: Water level forecasting in middle route of the south-to-north water diversion project (MRP) based on long short-term memory (LSTM). China Rural Water Hydropower (10), 189–193 (2020)
Ma, Z., Leung, J.Y.: Integration of deep learning and data analytics for SAGD temperature and production analysis. Comput. Geosci. 24(3), 1239–1255 (2020). https://doi.org/10.1007/s10596-020-09940-x
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Zheng, C., Wu, Y., Chen, Z., Wang, K., Zhang, L. (2021). A Load Forecasting Method of Power Grid Host Based on SARIMA-GRU Model. In: Cai, Z., Li, J., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2021. Communications in Computer and Information Science, vol 1494. Springer, Singapore. https://doi.org/10.1007/978-981-16-7443-3_9
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DOI: https://doi.org/10.1007/978-981-16-7443-3_9
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