Abstract: During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, which gather electrical usage data from customers of the utility system. The system uses the set of input signals and a projection technique to produce projected loadshapes, which are associated with electricity usage in the utility system. Next, the system identifies a closest time period in a database containing recent empirically obtained load-related parameters for the utility system, wherein the load-related parameters in the closest time period are closest to a present set of load-related parameters for the utility system. The system then iteratively adjusts the projected loadshapes based on changes indicated by the load-related parameters in the closest time period until a magnitude of adjustments falls below a threshold. Finally, the system predicts electricity demand for the utility system based on the projected loadshapes.

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.