Group for Research in Decision Analysis

G-2017-39

Power capacity profile estimation for activity-based residential loads

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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.

, 15 pages