A scalable method for probabilistic short-term forecasting of individual households consumption in low voltage grids

Short-term individual household load forecasting is relevant for several applications and low voltage grid (LVG) stakeholders, e.g., for grid simulations, operation planning, congestion anticipation or advance payments. Electrical consumption at the household level is highly stochastic, point forecasting cannot capture this efficiently. To have insights about the uncertainty of the prediction, probabilistic methods should be developed. We propose a method to predict the half-hourly consumption of individual households one day ahead, based on a neural network, enhanced with empirical quantiles based on the point forecasts errors. The method is scalable thanks to its low computational requirements. Additionally, it requires only historical data and calendar features. Finally, the method is evaluated in a case study where it achieves state-of-the-art accuracy.