Online SARIMA applied for short-term electricity load forecasting

Short-term Load Forecasting (STLF) plays a crucial role in balancing the supply and demand of load dispatching operations and ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data str...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2024, Vol.54 (1), p.1003-1019
Main Authors: Anh, Nguyen Thi Ngoc, Anh, Nguyen Nhat, Thang, Tran Ngoc, Solanki, Vijender Kumar, Crespo, Rubén González, Dat, Nguyen Quang
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Data transmission
Datasets
Electrical loads
Electricity
Forecasting
Machine learning
Machines
Manufacturing
Mechanical Engineering
Methods
Neural networks
Power
Processes
Smart sensors
Time series
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Summary:Short-term Load Forecasting (STLF) plays a crucial role in balancing the supply and demand of load dispatching operations and ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast the hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-023-05230-y