Functional data analysis for peak shape forecasting

 

Peak load forecasting is critical for power system operations and planning. Electric utilities are increasingly adopting peak-shaving strategies as they look for sustainable solutions to improve operational excellence. During the past few decades, hourly load forecasts have been used in practice as the input to inform peak-shaving decisions. The BigDEAL Challenge 2022 first proposed the concept of peak (load) shape forecasting, which showed that a better hourly load forecast may not result in a better peak shape forecast. This paper tackles peak shape forecasting, an under-served aspect of peak load forecasting, which is critical for deploying those peak shaving programs. We adopt functional data analysis in exploring new approaches to peak shape forecasting. Using the data published by ISONE, the experiments show that the peak shape forecasts are more robust when compared with the state-of-the-art hourly forecasting models, reducing the errors by 14–18%. The experiment also demonstrates that no single model excels across all attributes, emphasizing the need to build task-specific models.

Cite

@unpublished{fdashape,
  author = {Shreyashi Shukla and Tao Hong and Rob J Hyndman},
  title  = {Functional data analysis for peak shape forecasting},
  url    = {https://robjhyndman.com/publications/fdashape},
  year   = {2024}
}