Using Evolutionary Programming to increase the accuracy of an ensemble model for energy forecasting

Abstract

Natural gas companies are always trying to increase the accuracy of their forecasts. We introduce evolutionary programming as an approach to forecast natural gas demand more accurately. The created Evolutionary Programming Engine and Evolutionary Programming Ensemble Model use the current GasDay models, along with weather and historical flow to create an overall forecast for the amount of natural gas a company will need to supply to their customers on a given day. The existing ensemble model uses the GasDay component models and then tunes their individual forecasts and combines them to create an overall forecast.

The inputs into the Evolutionary Programming Engine and Evolutionary Programming Ensemble Model were determined based on currently used inputs and domain knowledge about what variables are important for natural gas forecasting. The ensemble model design is based on if-statements that allow different equations to be used on different days to create a more accurate forecast, given the expected weather conditions.

This approach is compared to what GasDay currently uses based on a series of error metrics and comparisons on different types of weather days and during different months. Three different operating areas are evaluated, and the results show that the created Evolutionary Programming Ensemble Model is capable of creating improved forecasts compared to the existing ensemble model, as measured by Root Mean Square Error (RMSE) and Standard Error (Std Error). However, the if-statements in the ensemble models were not able to produce individually reasonable forecasts, which could potentially cause errant forecasts if a different set of if-statements are true on a given day.