Framework for collaborative intelligence in forecasting day-ahead electricity price

Sergio Beltrán, Alain Castro, Ion Irizar, Gorka Naveran and Imanol Yeregui

Applied Energy, 2022, vol. 306, issue PA, No S0306261921013398

Abstract: Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR(MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts.

Keywords: Augmented analytics; Automated machine learning; Ensemble models; Explainable artificial intelligence; Time series decomposition; Time series hybrid models (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2021.118049

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