Intelligent data preprocessing technique to facilitate loadshape forecasting for a utility system

Abstract

The system receives a set of load signals from an archive that contains historic load information gathered at various locations throughout an electrical grid, which distributes electrical power for the utility system. Next, the system applies a first difference function to the set of load signals to produce a set of difference signals. The system then performs a spike-detection operation on the set of difference signals to identify pairs of positive-negative and negative-positive spikes, which identify gaps in the set of load signals associated with periods of network disruption. Next, the system modifies the set of load signals by filling in each identified gap with projected load values determined by performing a localized loadshape forecasting operation based on the continuous load values immediately preceding the identified gap. Finally, the system forecasts electricity demand for the utility system based on the modified set of load signals.