Hourly energy consumption profiles are of primary interest for measures to apply to the dynamics of the energy system. Indeed, during the planning phase, the required data availability and their quality is essential for a successful scenarios' projection. As a matter of fact, the resolution of available data is not the requested one, especially in the field of their hourly distribution when the objective function is the production-demand matching for effective renewables integration. To fill this gap, there are several data analysis techniques but most of them require strong statistical skills and proper size of the original database. Referring to the built environment data, the monthly energy bills are the most common and easy to find source of data. This is why the authors in this paper propose, test and validate an expeditious mathematical method to extract the building energy demand on an hourly basis. A benchmark hourly profile is considered for a specific type of building, in this case an office one. The benchmark profile is used to normalize the consumption extracted from the 3 tariffs the bill is divided into, accounting for weekdays, Saturdays and Sundays. The calibration is carried out together with a sensitivity analysis of on-site solar electricity production. The method gives a predicted result with an average 25％ MAPE and a 32％ cvRMSE during one year of hourly profile reconstruction when compared with the measured data given by the Distributor System Operator (DSO).