Accurate day-ahead nominations of grid losses in electrical distribution networks are important to reduce the societal cost of these losses. We present a modification of the CatBoost ensemble-based system for day-ahead grid loss prediction detailed in Dalal et al. (2020), making four main changes. Base models predict on the log-space of the target, to ensure non-negative predictions. The model ensemble is changed to include different model types, for increased ensemble variance. Feature engineering is applied to consumption and weather forecasts, to improve base model performance. Finally, a non-negative least squares-based stacking method that uses as many available models as possible for each prediction is introduced, to achieve an improved model selection that is robust to missing data. When deployed for over three months in 2022, the resulting system reduced mean absolute error by 10.7% compared to the system from Dalal et al. (2020), a reduction from 5.05 to 4.51 MW. With no tuning of machine learning parameters, the system was also extended to three new grids, where it achieved similar relative error as on the old grids. Our system is robust and easily scalable, and our proposed stacking method could provide improved performance in applications outside grid loss.

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.