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Using hourly energy consumption data recorded by smart meters, retailers canestimate the day-ahead energy consumption of their customer portfolio. Deepneural networks are especially suited for this task as a huge amount ofhistorical consumption data is available from smart meter recordings to be usedfor model training. Probabilistic layers further enable the estimation of theuncertainty of the consumption forecasts. Here, we propose a method tocalculate hourly day-ahead energy consumption forecasts which include anestimation of the aleatoric uncertainty. To consider the statistical propertiesof energy consumption values, the aleatoric uncertainty is modeled usinglognormal distributions whose parameters are calculated by deep neuralnetworks. As a result, predictions of the hourly day-ahead energy consumptionof single customers are represented by random variables drawn from lognormaldistributions obtained as output from the neural network. We furtherdemonstrate, how these random variables corresponding to single customers canbe aggregated to probabilistic forecasts of customer portfolios of arbitrarycomposition.

Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation