Day-Ahead Forecasting of Losses in the Distribution Network
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https://hdl.handle.net/11250/2724630Utgivelsesdato
2020Metadata
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Originalversjon
10.1609/aaai.v34i08.7018Sammendrag
We present a commercially deployed machine learning system that automates the day-ahead nomination of the expected grid loss for a Norwegian utility company. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduces the MAE with 41% from 3.68 MW to 2.17 MW per hour from mid July to mid October. It is robust and reduces manual work.