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Advances in Time Series Forecasting Development for Power Systems’ Operation with MLOps

Author

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  • Gonca Gürses-Tran

    (Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, 52064 Aachen, Germany
    Current address: Department of Electrical Engineering, RWTH Aachen University, Mathieustr. 10, 52074 Aachen, Germany.)

  • Antonello Monti

    (Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, 52064 Aachen, Germany
    Center for Digital Energy Aachen, Fraunhofer FIT, 52074 Aachen, Germany
    Current address: Department of Electrical Engineering, RWTH Aachen University, Mathieustr. 10, 52074 Aachen, Germany.)

Abstract

Forecast developers predominantly assess residuals and error statistics when tuning the targeted model’s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The analysis of the power system wholesale market allows us to translate a time series forecast method’s quality to its respective business value. For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators’ grid activities and balancing parties’ scheduling behavior. Such forecasting tasks can hardly be solved with model-driven approaches because of the large solution space and non-convexity of the optimization problem. Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF , which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet .

Suggested Citation

  • Gonca Gürses-Tran & Antonello Monti, 2022. "Advances in Time Series Forecasting Development for Power Systems’ Operation with MLOps," Forecasting, MDPI, vol. 4(2), pages 1-24, May.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:2:p:28-524:d:825213
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    References listed on IDEAS

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    Cited by:

    1. Niccolò Borghi & Giorgio Guariso & Matteo Sangiorgio, 2024. "Forecasting Convective Storms Trajectory and Intensity by Neural Networks," Forecasting, MDPI, vol. 6(2), pages 1-17, May.

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