Abstract
In reduce the use of non-renewable energy, the use of renewable energy is increasing day by day. In recent years, with the strong support of the state, renewable energy has been applied in various industries. Renewable energy generates a considerable amount of electricity, which brings us huge economic benefits but also brings certain problems. For example, the instability of the power generation system, the scheduling, and distribution of power, etc. Therefore, the analysis of the massive power data generated by the power system has become particularly important. Effective processing and forecasting of these power data can not only improve the efficiency and performance of the power system but also enable effective power dispatching and deployment. At the same time, it can ensure the safety of industrial and family users and ensure social stability. Machine learning has been widely used in various fields and achieved good performance in recent years. Therefore, many researchers have begun to use machine learning to predict power data. Therefore, we provide a preliminary overview of the history and evolution of machine learning-based power data analysis and forecasting from the perspective of bibliometrics.
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References
Sun, B., Geng, R., Zhang, L., Li, S., Shen, T., Ma, L.: Securing 6G-enabled IoT/IoV networks by machine learning and data fusion. EURASIP J. Wirel. Commun. Netw. 2022 (2022)
Sun, B., Geng, R., Yuan, X., Shen, T.: Prediction of emergency mobility under diverse IoT availability. EAI Endorsed Trans. Pervasive Health Technol. 8(4), e2 (2022)
de Alencar, D.B., et al.: Different models for forecasting wind power generation: case study. Energies 10(12), 2017 (1976)
Hanifi, S., Liu, X., Lin, Z., Lotfian, S.: A critical review of wind power forecasting methods-past, present and future. Energies 13(15), 3764 (2020)
Alhussein, M., Aurangzeb, K., Haider, S.I.: Hybrid CNN-LSTM model for short-term individual household load forecasting. IEEE Access 8, 180544–180557 (2020)
Lee, D., Kim, K.: Recurrent neural network-based hourly prediction of photovoltaic power output using meteorological information. Energies 12(2), 215 (2019)
Sun, B., Geng, R., Shen, T., Xu, Y., Bi, S.: Dynamic emergency transit forecasting with IoT sequential data. Mob. Netw. Appl. 1–15 (2022)
Xiao, Y., Shao, H., Han, S.Y., Huo, Z., Wan, J.: Novel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE/ASME Trans. Mechatron. 27(6), 5254–5263 (2022)
Yan, S., Shao, H., Xiao, Y., Liu, B., Wan, J.: Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises. Robotics Comput. Integr. Manuf. 79, 102441 (2023)
Sun, B., Ma, L., Shen, T., Geng, R., Zhou, Y., Tian, Y.: A robust data-driven method for multiseasonality and heteroscedasticity in time series preprocessing. Wirel. Commun. Mob. Comput. 2021, 6692390:1–6692390:11 (2021)
Aria, M., Cuccurullo, C.: Bibliometrix: an R-tool for comprehensive science mapping analysis. J. Informet. 11(4), 959–975 (2017)
Wang, H.Z., Wang, G.B., Li, G.Q., Peng, J.C., Liu, Y.T.: Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl. Energy 182, 80–93 (2016)
Liu, Z., Jiang, P., Zhang, L., Niu, X.: A combined forecasting model for time series: application to short-term wind speed forecasting. Appl. Energy 259, 114137 (2020)
Wang, J.-Z., Wang, Y., Jiang, P.: The study and application of a novel hybrid forecasting model - a case study of wind speed forecasting in china. Appl. Energy 143, 472–488 (2015)
Inman, R.H., Pedro, H.T.C., Coimbra, C.F.M.: Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39(6), 535–576 (2013)
Das, U.K., et al.: Forecasting of photovoltaic power generation and model optimization: a review. Renew. Sustain. Energy Rev. 81, 912–928 (2018)
Sun, B., Cheng, W., Bai, G., Goswami, P.: Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection. Tehnicki Vjesnik-Technical Gazette 24, 1597–1607 (2017)
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Li, L., Zhang, L., Sun, B., Dong, B., Xu, K. (2024). Power Sequencial Data - Forecasting Trend. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_32
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