17th AIAI 2021, 25 - 27 June 2021, Greece

Explainable needn’t be (much) less accurate: evaluating an explainable AI dashboard for energy forecasting

Ana Grimaldo, Jasminko Novak

Abstract:

  This paper presents the evaluation results of an improved version of an interactive tool for energy demand and supply forecasting, based on the combination of ex-plainable machine learning with visual analytics. The prototype applies a kNN algo-rithm to forecast energy demand and supply from historical data (consumption, pro-duction, weather) and presents the results in an interactive visual dashboard. The dashboard allows the user to understand how the forecast relates to the input param-eters and to analyse different forecast alternatives. It provides small utilities not fa-miliar with AI with an easily understandable, while sufficiently accurate tool for en-ergy forecasting in prosumer scenarios. The evaluation of the forecast accuracy has shown our method to be only 0.26%-1.73% less accurate than more sophisticated, but less explainable machine learning methods. Moreover, the achieved accuracy (MAPE 5.06%) is sufficient for practical needs of the application scenario. The evaluation with potential end-users also provided positive results regarding the usa-bility, understandability and usefulness for the intended application context.  

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