Improving Energy Retrofit Decisions by Including Uncertainty in the Energy Modelling Process

Currently, many investment decisions concerning energy retrofits are made directly based on the outcomes of energy simulations. However, there are various uncertainties inherent in the energy retrofit assessment process, both at the energy simulation and life cycle cost analysis (LCCA) levels, which can result in inaccuracy of energy performance forecasts and therefore, inappropriate investment decisions. Through a case study, this paper presents a procedure for deriving and including the uncertainty associated with various factors in energy retrofit option assessment and clearly demonstrates how to generate probability distributions for final financial outcomes required for investment decision-making such as Net Present Value (NPV) and Internal Rate of Return (IRR). These distributions provide decision makers with more insight into the risks associated with achieving the expected outcomes. The simulation process proposed in this paper could be used by modelers to improve the level of confidence associated with simulation outcomes and enhance the quality of investment decisions concerning energy retrofit. An existing office building is selected and multiple calibrated energy base models are developed to evaluate a combination of lighting controls as a new energy retrofit option. The paper demonstrates the calibration process of the base models and a LCCA of the lighting controls package. Analysis was conducted to examine how evaluating retrofit options with multiple base models could impact the financial outcomes and improve the final investment decisions. The financial metrics are compared with the results of modeling using a single base model. The results show that this approach could have the potential to and may alter a retrofit decision from 'no go' to 'go'.