Electric loadshape forecasting based on smart meter signals

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for forecasting electricity demand for a utility system, the method comprising:

• receiving a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter in the set gathers electrical usage data from a customer of the utility system;
• using the set of input signals to train an inferential model, which 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.

A system that forecasts electricity demand for a utility system, comprising:

• at least one processor and at least one associated memory;
• and
• a forecasting mechanism that executes on the at least one processor, wherein during operation, the forecasting mechanism:
• receives a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter in the set gathers electrical usage data from a customer of the utility system;
• uses the set of input signals to train an inferential model, which learns correlations among the set of input signals;
• uses 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;
• uses 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
• projects the set of synthesized signals into the future to produce a forecast for the electricity demand for the utility system.