Calculation methodologies for equivalent temperature applied to energy demand forecasting

In the face of growing challenges in the electrical sector, such as demand variability and climate change, understanding and forecasting electrical variables become critical for distribuition companies. This work presents a set of methodologies for calculating equivalent temperatures in large geographic areas, which can be used in forecasting models to understand the behavior of electrical variables such as demand and load, thus assisting energy distributors. For this purpose, six calculation methodologies were developed, with emphasis on the one based on linear regression. With this parameter, the electric company improves communication about consumption variations with stakeholders, in addition to avoiding the problem of curse of dimensionality in the development of consumption forecasting models. The case study, related to a distribution company in Brazil, involves forecasting both own load and total load. The study shows that using this temperature data can improve the forecast’s performance, whether using statistical or machine learning models. The best results indicate a MAPE of 2.0% for the Arima model and 2.4% for the Random Forest Regressor. Finally, it is essential to mention that the developed methodologies are applicable to other weather variables, such as precipitation, solar radiation, air humidity, and wind speed.