Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage an input variable. In order quantify uncertainty of EDF effectively, this pap...

Abstract. Poisson conditional autoregressive model of spatio-temporal data is proposed. Markov property and probabilistic characteristics of this model are presented. Algorithms for maximum likelihood estimation of the model parameters are constructed. Optimal forecasting statistic minimizing probability of forecast error is given. The “plug-in” principle based on ML-estimators is used for fore...