A renewed version of the currently-in-use widespread degree-day method is investigated in this master thesis. By reviewing the literature and understanding the degree-day theory it is explicit that using a constant base temperature and analysing a low-energy building on a monthly scale lead to misleading results as it does not take into account specific knowledge of the gain to loss ratio of the building. Therefore this study presents a renewed degree-day method which calculates monthly base temperatures. Through the analysis of the low-energy office building Ostarkade, the space heating consumption estimate is compared with the measurement. It is also compared with two other estimations using constant base temperatures of 12°C and 15°C. The current degree-day method largely under-predicts the space heat consumption in the low-heating season for both base temperatures. The new estimation presents better results in all months of the year and especially in spring and autumn months. The model enables calculating hourly space heating consumptions with a maximum error of 8.8% on monthly total estimates. The model is flexible and allows sensitivity analyses on the input parameters. Also for the first time the impact of the user on the space heating consumption is investigated using the Morris method. The uncertainty analysis has shown that a variation in +/-2°C of the monthly set-point temperature impacts the monthly space heating consumptions up to +10% in colder months. Therefore the calculation of monthly set-point temperatures derived from yearly values need further research. The model also calculates average monthly internal temperatures and average monthly heat gains. This renewed degree-day method presents a basis for future work on monthly base temperature estimates for low-energy buildings. This model offers a new tool to estimate the hourly space heating consumption of low-energy buildings based on monthly base temperatures calculations with a higher accuracy than the current degree-day method.