This paper proposes a framework to determine day-ahead capacity profiles that account for the stochastic demand generated by user behavior in smart buildings. The user selects a level of capacity per time frame in the context of flexible time-and-level-of-use pricing. We generate the consumption scenarios by aggregating historical data. We also present two approaches to determine the required capacity given the demand. In the first approach, we solve a two-stage optimization model under the assumption that the start time probability distributions of the loads are known. In the second approach, we use a greedy-type algorithm that analyzes a set of previous consumption profiles to estimate future capacity requirements. We report experiments to validate the proposed approaches.