Energy demand prediction is a strategic tool for distribution companies due to the need to contract the amount of use of the transmission and distribution systems. However, most of the literature focuses on forecasting rather than simulation. The generation of future scenarios is essential to capture the inherent uncertainty of the process and to allow for a risk-averse decision making framework. The first of this two-paper series proposes a methodology to simulate long-term, low-frequency energy consumption scenarios through state-space models. An open-source Julia package containing the implementation of the time series state-space modeling, Kalman filter and maximum likelihood estimation is made available. Finally, a case study with real data from the Brazilian power system is presented.