Modelización de la demanda de energía eléctrica: Más allá de la normalidad

This work proposes a model of electrical energy demand based on time series methods and semi-nonparametric statistics (SNP). This allows knowing not only the expected value of the demand but also its probability distribution so that, by calculating metrics such as the Quantile Risk Metrics, decisions can be made based on less or more extreme values favorable than the expected value. The results show that in the case of electricity demand in the Colombian market between 2000 and 2018, the probability distribution of the average daily demand is leptokurtic. That is, extreme events occur more frequently than those assumed by a normal distribution. Thus, the Gaussian distribution assumption leads to undervaluation of risk in terms of undervaluation of the frequency of extreme values.