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

Gonca Gürses-Tran and Antonello Monti
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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
Antonello Monti: Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University, 52064 Aachen, Germany

Forecasting, 2022, vol. 4, issue 2, 1-24

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 .

Keywords: MLOps; probabilistic load forecasting; uncertainty; grid operation; congestion management (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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