Energy efficient buildings rely on simulation to predict energy performance. However, problems associated with simulation tools can lead to surprises when discrepancies are found between actual and predicted building energy performance; this frustrates building owners, investors, and designers. A probabilistic method of risk assessment for the calculation of energy use intensity and total utility cost in energy performance has been developed. Sensitive and uncertain parameters were selected and given a probability distribution instead of one fixed value for the simulations. Latin hypercube sampling was used to generate input combinations with parameter values picked stochastically from distributions based on the Monte Carlo method. With these input combinations, 10;000 simulations on seven distributed parameters were run using a cloud processing service. The output data, energy use intensity and energy cost, were analyzed using curve-fitting techniques to find a best-fit distribution, which could be used for risk analysis of energy performance and cost. The results illustrate the probability and reliability of prediction within a specific range. Instead of relying on a single value, these curves would help designers better evaluate design alternatives, and the probability distribution of energy performance and cost would be useful in making decisions about investments for energy efficient projects. (C) 2015 Elsevier B.V. All rights reserved.