Globally, water demand is rising and resources are diminishing. In the context of climate change and a growing world population, a further increase in water scarcity seems inevitable. Aiming towards a sustainable future, water should be used as efficiently as possible by minimizing water losses, which can be higher than 50% in some drinking water networks. To minimize water losses it is crucial to detect, localize and repair leaks as soon as possible. Leaks can be efficiently and automatically tracked down in the early stages by using leak detection and localization techniques. These techniques are based on coupling information from flow and pressure measurements with the hydraulic model of the drinking water network. The success of such methods depends to a great extend on the estimation of the uncertain water demand in the area. The water demand within a hydraulic model is usually estimated in a deterministic fashion, which lacks the ability to realistically describe the fluctuations in water demand. To include realistic fluctuations in water demand, this study proposes a novel approach by estimating the water demand in a stochastic way by simulating it with SIMDEUM. This stochastic demand model uses information of water users and water-use appliances from Dutch statistics to simulate realistic domestic drinking water demands and its stochastic variations. With this approach, this study aims to assess the influence of a realistic stochastic demand model on the robustness of leak detection and localization algorithms. The applied case study is a residential area in the Netherlands, consisting of a drinking water network with an inflow and six pressure sensors. The corresponding hydraulic model uses stochastic demand loading conditions as inputs. The model represents the network reliably when the SIMDEUM software with local statistics is used instead of average Dutch household statistics. By conducting a Monte Carlo analysis, the influence of stochastic demand on the variability of simulated flow and pressures is determined. Artificial leaks are simulated and the influence of stochastic demand on the performance of leak detection and leak localization is analyzed. The leak detection method consists of setting up confidence intervals per sensor from Monte Carlo simulations and checking whether data falls within these intervals. The leak localization method is based on simulating artificial leaks on all possible locations in the model and comparing the resulting simulated pressures for each simulation to the observed data by using Pearson’s correlation coefficient. The position of the leak in the simulation most similar to the observations is most likely to be near the real leak position. The results show that the stochastic demand variability is strongly linked to the performance of leak detection and localization. The leak detection and localization is most sensitive during the night due to low nocturnal demand fluctuations and least sensitive during the morning peak, when diurnal demand fluctuations are highest. Moreover, the results show that the position and size of the leak significantly influence the performance of leak detection and localization. This study shows that stochastic water demand can be used to quantify the influence of realistic demand variations on the performance of leak detection and localization. Hence, it is recommended to assess the robustness of more leak detection and localization techniques by using stochastic water demand. Furthermore, it gives insight into the expected variability in pressure throughout the network, hence, can prove to be useful in optimal sensor placement.