Energy efficient buildings: a method of probabilistic risk assessment using building energy simulation

2015-04-08Even though the design of energy efficient buildings provides an excellent opportunity to achieve large scale energy reductions, the process of achieving this still has difficulties. As an essential and heavily relied upon tool in the design process, software simulation is used to predict building energy performance. However, there are problems associated with simulation tools including the following: estimated data is usually used instead of real data, a lack of accurate occupant schedule and behavior models, inaccurate weather data, and unrealistic performance expectations for mechanical equipment. Simulations may not even be used for accurate predictions of energy performance, but instead just for comparison of alternatives and payback periods, compliance with energy code protocols, and perhaps just general estimates of energy usage. All these problems can lead to surprises when discrepancies are found between actual and predicted building energy performance, which frustrates the building owners, designers, and investors. Research about energy model calibration and the uncertainty of single parameters (e.g. weather data and occupancy), has been done before. Less has been done for incorporating overall risk assessment into energy simulations that takes multiple factors into consideration. ❧ A probabilistic method of risk assessment in energy performance simulations has been proposed and tested. Literature review and discussion with professionals were conducted to decide the parameters that produce most uncertainties in simulations, followed by Differential Sensitivity Analysis (DSA) to identify the most influential parameters among them. Each selected parameter was given a range of values and probability. These were used in simulations in a distribution instead of one fixed value, either a continuous distribution or discrete distribution. Latin Hypercube Sampling (LHS) was used to generate input combinations with parameter values picked stochastically from distributions based on the Monte Carlo method. With these input combinations, thousands of simulations were run using a cloud processing service. Output data was collected and analyzed using a curve-fitting technique to find a best fitted distribution, which could be used for risk and uncertainty analysis of both energy performance and cost information. ❧ A DOE reference building has been used to test this methodology. Among 17 uncertain parameters, seven more influential ones were identified by DSA. 10,000 simulations were run with these seven distributed parameters (weather file, cooling set-point, cooling supply air temperature, equipment density, lighting density, fan overall efficiency, and coil cooling COP). The output data, energy usage intensity (EUI) and energy cost, were fitted into distribution for risk analysis. The results shows the probability and reliability of prediction within a certain range, and both EUI and energy cost could possibly deviate from original prediction in a large percentage. ❧ This methodology has shown that a tool can be developed that expresses the EUI and energy cost of a building simulation as a distribution of likely values rather than a single value. The intent is that a finalized tool would help designers to better evaluate design alternatives and that the results, probability distribution of energy performance and cost, would be useful in making decisions about investments in building energy efficient projects, both new and retrofits