A computer-implemented method for forecasting electricity demand for a utility system, comprising: receiving 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; preprocessing the set of load signals by, applying a first difference function to the set of load signals to produce a set of difference signals, performing 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, and modifying 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; and forecasting electricity demand for the utility system based on the preprocessed set of load signals, wherein forecasting the electricity demand for the utility system comprises: using a set of input signals, which includes the set of load signals and other input signals, to train an inferential model that learns correlations among the set of input signals; using the inferential model to produce a set of inferential signals, wherein the inferential model produces an inferential signal for each input signal in the set of input signals; using a Fourier-based decomposition-and-reconstruction technique, which decomposes each signal in the set of inferential signals into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals; and projecting the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system. receiving 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; preprocessing the set of load signals by, applying a first difference function to the set of load signals to produce a set of difference signals, performing 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, and modifying 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; and applying a first difference function to the set of load signals to produce a set of difference signals, performing 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, and modifying 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; and forecasting electricity demand for the utility system based on the preprocessed set of load signals, wherein forecasting the electricity demand for the utility system comprises: using a set of input signals, which includes the set of load signals and other input signals, to train an inferential model that learns correlations among the set of input signals; using the inferential model to produce a set of inferential signals, wherein the inferential model produces an inferential signal for each input signal in the set of input signals; using a Fourier-based decomposition-and-reconstruction technique, which decomposes each signal in the set of inferential signals into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals; and projecting the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system. using a set of input signals, which includes the set of load signals and other input signals, to train an inferential model that learns correlations among the set of input signals; using the inferential model to produce a set of inferential signals, wherein the inferential model produces an inferential signal for each input signal in the set of input signals; using a Fourier-based decomposition-and-reconstruction technique, which decomposes each signal in the set of inferential signals into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals; and projecting the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system.