Abstract
In building retrofit projects, dynamic simulations are performed to simulate building performance. Uncertainty may negatively affect model calibration and predicted lighting energy savings, which increases the chance of default on performance-based contracts. Therefore, the aim of this paper is to develop a simulation-based method that can analyze lighting performance risk in lighting retrofit decisions. The model uses a surrogate model, which is constructed by adaptively selecting sample points and generating approximation surfaces with fast computing time. The surrogate model is a replacement of the computation intensive process. A statistical method is developed to generate extreme weather profile based on the 20-year historical weather data. A stochastic occupancy model was created using actual occupancy data to generate realistic occupancy patterns. Energy usage of lighting, and heating, ventilation, and air conditioning (HVAC) is simulated using EnergyPlus. The method can evaluate the influence of different risk factors (e.g., variation of luminaire input wattage, varying weather conditions) on lighting and HVAC energy consumption and lighting electricity demand. Probability distributions are generated to quantify the risk values. A case study was conducted to demonstrate and validate the methods. The surrogate model is a good solution for quantifying the risk factors and probability distribution of the building performance.
Highlights
In a retrofit decision process, professionals perform energy audits and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions [1]
The surrogate model serves as a replacement for computationally expensive simulation and provides a fast calculation of the risk factors sampled using
Dynamic simulation can be performed for energy efficient building retrofits in order to predict the effect of retrofit interventions
Summary
In a retrofit decision process, professionals perform energy audits and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions [1]. Retrofit decisions can be evaluated using multi-objective optimization [2,3]. Deterministic models do not provide insights into underperforming risks associated with each retrofit intervention. A more reliable approach is performing sensitivity analysis to quantify the influence of different risk factors on building operation performance [4,5]. A distribution is chosen for a number of inputs and numerous models are generated to simulate and determine statistics of important outputs. Sensitivity analysis for thermal parameter ranking can help to choose the envelope material type
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