control large databases, scientists have started to record large datasets with detailed data on occupancy of household dwellers and electricity consumption. Markov chain theory have recently been started to be applied in connection with load modeling, analysis of data and creation of new synthetic datasets. Capability to make transition probabilities dependent on the various factors such as, - type of household, time of electricity use, etc, - advanced the use of Markov chains among researchers and allowed to develop demand forecasting and financial accounting. The aims of the paper are 1) to estimate transition probabilities of Markov chain that describes households’ electricity demand, and 2) to show that such probabilities can be used to generate new synthetic patterns. Statistical validation of the predicted electricity demand against metered data is also provided. Both in-sample and out-of-sample tests showed that Markov chain can be used to reproduce realistic household consumption with high precision, including variation between seasons and weekdays/weekends. Scarce availability of similar models increases the importance and added value of the paper.