Forecasting the energy demand is very crucial for a variety of reasons. Be it electrical, thermal, chemical, or other types of energy; forecasting the demand accurately in a timely manner helps reducing operational, logistics and purchasing costs.

 

Energy demand depends on many factors. Some of them are direct factors such as industrial production requirements, population, net capital income or energy unit prices. Others are indirect such as the amount of natural gas required to produce electricity, which depends on the amount of electricity produced from cheaper resources like wind or water highly effected by weather conditions.

 

To make accurate energy demand forecasts, one needs to identify all the complex direct and indirect factors affecting consumption and model these relations in such a way that each factor is considered fully and correctly. This may require building separate complex deep learning and/or statistical models and combine them at the right level at the right time.

 

At Predy, we have developed many energy demand forecasting models and obtained very promising results. Our forecast models achieved much lower average percentage error (APE) for different spatial and temporal resolutions compared to standard forecast models to predict natural gas consumption, electricity demand and industrial usage.