Forecast of Distributed Energy Generation and Consumption in a Partially Observable Electrical Grid: A Machine Learning Approach

Abstract

With a radical energy transition fostered by the increased deployment of renewable non-programmable energy sources over conventional ones, the forecasting of distributed energy production and consumption is becoming a cornerstone to ensure grid security and efficient operational planning. Due to the distributed and fragmented design of such systems, real-time observability of Distributed Generation operations beyond the Transmission System Operator domain is not always granted. In this context, we propose a Machine Learning pipeline for forecasting distributed energy production and consumption in an electrical grid at the HV distribution substation level, where data from distributed generation is partially observable. The proposed methodology is validated on real data for a large Italian region. Results show that the proposed model is able to predict up to 7 days ahead the amount of load and distributed generation (and the net power flux by difference) at each HV distribution substation with a 24%-44% mean gain in out-of-sample accuracy against a non-naive baseline model, paving the way to advanced and more efficient power system management.