Utility analysts at a large gas and electricity company developed models that predict energy demand for the following day. The models enable power grid operators to optimize resources and schedule power plant generation. Each model accesses a central database for historical power consumption and price data, weather forecasts, and parameters for each power plant, including maximum power out, efficiency, costs, and all the operation constraints that influence the plant dispatch. Analysts looked for a model that provided a low mean absolute percent error (MAPE) to the testing data set. After trying several different types of regression models, it was determined that neural networks provided the lowest MAPE due to their ability to capture the nonlinear behavior of the system.
