Building performance simulation (BPS) enables users to predict the demand reductions achieved by energy conservation measures (ECMs). Identifying an optimal set of ECMs in combination is complex due to interaction effects. This creates a combinatorial problem where every ECM combination needs be simulated to identify the optimum with certainty. To avoid the computational burden of running separate simulations for each ECM combination, approximate approaches for predicting the joint effects of ECMs based on single ECM simulations have been proposed in literature: linear-addition and log-addition of savings. These reduced-order approaches are very rapid compared to BPS, but their accuracy is not well characterized. This paper compares ECM energy savings estimated by BPS with the linear and log-additive approaches and a new reduced-order approach: log-additive decomposition. An existing library of energy models and ECMs (Hamilton, et al., 2014) representing Philadelphia medium-sized office buildings is utilized to compare the performance of each approach not only to each other but also to BPS. Overall, log additive decomposition performs well (prediction error similar to 10%) followed by log-addition (prediction error similar to 20% to 30%), which outperform linear addition (prediction error often exceeding 50%). Compared to BPS, the computational cost of each is 0.018%, 0.014%, and 0.004% respectively. (C) 2018 Elsevier B.V. All rights reserved.