Research Article
Power Sequencial Data - Forecasting Trend
@INPROCEEDINGS{10.1007/978-3-031-50580-5_32, author={Lejie Li and Lu Zhang and Bin Sun and Benjie Dong and Kaining Xu}, title={Power Sequencial Data - Forecasting Trend}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Electricity Data analytics Data prediction Machine learning}, doi={10.1007/978-3-031-50580-5_32} }- Lejie Li
Lu Zhang
Bin Sun
Benjie Dong
Kaining Xu
Year: 2024
Power Sequencial Data - Forecasting Trend
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_32
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.
