Predicting seasonal patterns of energy production: A grey seasonal trend least squares support vector machine

作者：

Highlights：

• A grey seasonal trend support vector machine for energy forecasting is proposed.

• The model can efficiently identify the seasonal fluctuations in energy data.

• The model is powerful to predict China’s energy production.

• The predictive performance of the new model is much superior to the benchmarks.

摘要

•A grey seasonal trend support vector machine for energy forecasting is proposed.•The model can efficiently identify the seasonal fluctuations in energy data.•The model is powerful to predict China’s energy production.•The predictive performance of the new model is much superior to the benchmarks.

论文关键词：Energy forecasting,Seasonal fluctuations,Least squares support vector machine,Grey prediction model,China

论文评审过程：Received 10 May 2022, Revised 10 September 2022, Accepted 17 September 2022, Available online 24 September 2022, Version of Record 6 October 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118874