# Forecasting Irregular Seasonal Power Consumption.
## 1 Introduction  
Demand management is a primary process in the development of industrial activity. Distribution companies must ensure a supply is provided at a reasonable cost, and for this reason, they need to manage resources efficiently. The use of electrical prediction models contributes to their management of the distribution lines by offering tools to estimate future demand with great precision. The techniques allow for forecasting based on time series using statistical models or artificial intelligence (AI). The most widely used univariate forecasting tools for electricity demand can be classified into three broad groups [1]: fundamental models, statistical models, and computational models. There is growing interest in the use of computational models, although the most widely used models are statistical models, both exponential smoothing models and autoregressive integrated moving average (ARIMA) models. The fundamental models are made up of hybrid models that introduce all the possible physical variables, adopting a complex relationship between them and also using the techniques of statistical models. Computational models are based on AI and emulate natural behaviors through the use of mathematical models. These are algorithms whose learning is automatic and arepart of the science of Machine Learning [2]. At present, deep learning techniques represent an evolution and have found applications in demand forecasting, especially in areas where prediction is difficult, such as renewable energies [3]. The most widely used techniques for electricity demand are artificial neural networks (ANN) [4], particularly non-linear autoregressive neural networks with exogenous variables (NARX) [5,6]. Support vector machines (SVM) [7] and bagged regression trees (BRT) [8] also stand out, and these occasionally apply fuzzy logic [9]. Electricity demand series show stochastic behavior, and they have traditionally been modeled using statistical methods. The ARIMA models are considered to be the econometric models par excellence. The Box-Jenkins methodology [10] is used to determine which ARIMA model to use, although some authors [11] state that simpler methods are better than this methodology at providing forecasts. The application of ARIMA models to demand is usually carried out in a general way in Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX) models [12, 13, 14] in which exogenous variables are included to improve demand. The introduction of two seasonalities allows substantial improvement in the predictions of these models [15]. State-space models (SSM) are a form of exponential smoothing representation. They are commonly applied to demand [16], especially since the introduction of the Kalman filter (see [17]). They also allow the introduction of various seasonalities in a complex way [18] and with covariates [19]. [PERSON] and [PERSON] include modifications that include adjustment of the error using autoregressive moving average (ARMA) models and with Box-Cox transformations (BATS [20]) and trigonometric seasonality (TBATS [18]). Other very common smoothing techniques are the Holt-Winters models [20]. These models are excellent predictors for time series with marked seasonality [1,21]. The inclusion of more seasonality [22, 23, 24] improves their forecasts, leading to the development of multiple seasonal Holt-Winters models (nHWT). [PERSON] et al. [25] introduce discrete seasonality that takes into account seasonalities whose occurrences are not regular (nHWT-DIMS models). The current trend is to create hybrid models in which traditional techniques are combined with machine learning [26,27]. An example can be found in [28], which applies an exponential smoothing method and neural networks to divide the forecasting process between a linear part and a non-linear part. The use of wavelets for irregular series has been combined with ARIMA models [29], Holt-Winters models [30], or ANN [31].