Net zero energy buildings (NZEBs) have been widely considered to be an effective solution to the increasing energy and environmental problems. Most conventional design methods for NZEB systems are based on deterministic data/information and have not systematically considered the significant uncertainty impacts. Consequently, the conventional design methods lead to popular oversized problems in practice. Meanwhile, NZEB system design methods need to consider customers’ actual performance preferences but few existing methods can take account of them. Therefore, this study proposes a multi-criteria system design optimization for NZEBs under uncertainties. In the study, three performance criteria are used to evaluate the overall NZEB system performance based on user-defined weighted factors. Case studies are conducted to demonstrate the effectiveness of the proposed method.

Net zero energy buildings (NZEBs) are promising to mitigate the increasing energy and environmental problems. For NZEBs, annual energy balance between renewable energy generation and building energy consumption is an essential and fundamental requirement. Conventional RES (renewable energy system) design methods for NZEBs have not systematically considered uncertainties associated with building energy generation and consumption. As a result, either the annual energy balance cannot be achieved or the initial investment of RES is unnecessarily large. Meanwhile, the uncertainties also have significant impacts on NZEB power mismatch which can cause severe grid stress. In order to overcome the above challenges, this study proposes a multi-criterion RES design optimization method for NZEBs under uncertainties. Under the uncertainties, Monte Carlo simulations have been employed to estimate the annual energy balance and the grid stress caused by power mismatch. Three criteria, namely the annual energy balance reliability, the grid stress and the initial investment, are used to evaluate the overall RES design performance based on user-defined weighted factors. A case study has demonstrated the effectiveness of the proposed method in optimizing the size of RES under uncertainties.

Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-model-based system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally selected. Cases studies with 1331 design options have validated the proposed method for 10,000 randomly produced decision scenarios (i.e., users’ preferences to the design criteria). The results show that the established response surface models reasonably predict the design criteria with errors no greater than 3.5% at a cumulative probability of 95%. The proposed method reduces the number of Monte Carlos simulations by 97.8%, and robustly sorts out top 1.1% design options in expectation. With the largely reduced Monte Carlo simulations and high overall performance of the selected design option, the proposed method provides a practical and efficient means for system sizing of nearly/net ZEBs under uncertainty.