Electric loadshape forecasting for utility systems that support dynamic power pricing

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 in a utility system that supports dynamic pricing, the method comprising:

• receiving 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;
• using the set of input signals and a projection technique to produce projected loadshapes, which are associated with electricity usage in the utility system;
• optimizing the projected loadshapes to account for nonlinear effects resulting from the dynamic pricing by,
• identifying 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, and
• iteratively adjusting 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;
• and
• predicting electricity demand for the utility system based on the projected loadshapes.

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

• at least one processor and at least one associated memory;
• and
• a prediction mechanism that executes on the at least one processor, wherein during operation, the prediction mechanism:
• 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;
• uses the set of input signals and a projection technique to produce projected loadshapes, which are associated with electricity usage in the utility system;
• optimizes the projected loadshapes to account for nonlinear effects resulting from the dynamic pricing by, wherein optimizing the projected loadshapes involves,
• identifying 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, and
• iteratively adjusting 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;
• and
• predicts electricity demand for the utility system based on the projected loadshapes.