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Statistical models can be used as surrogates of detailed simulation models. Their key advantage is that they are evaluated at low computational cost which can remove computational barriers in building performance simulation. This comprehensive review discusses significant publications in sustainable building design research where surrogate modelling was applied. First, we familiarize the reader with the field and begin by explaining the use of surrogate modelling for building design with regard to applications in the conceptual design stage, for sensitivity and uncertainty analysis, and for building design optimisation. This is complemented with practical instructions on the steps required to derive a surrogate model. Next, publications in the field are discussed and significant methodological findings highlighted. We have aggregated 57 studies in a comprehensive table with details on objective, sampling strategy and surrogate model type. Based on the literature major research trends are extracted and useful practical aspects outlined. As surrogate modelling may contribute to many sustainable building design problems, this review summarizes and aggregates past successes, and serves as practical guide to make surrogate modelling accessible for future researchers.

This study investigates the performance of a wide selection of single objective black-box optimization algorithms (optimizers) when applied to a large set of building energy simulation problems from the literature. Optimizers include randomized, deterministic and model-based algorithms. We also study the impact of tunable hyper-parameters on performance for some of the optimizers. We apply benchmark metrics from the literature and develop a new metric based on ranks, which is especially useful for comparing small differences in optimal cost values. Our extensive experimental setup allows us to draw generalized conclusions on convergence times, stability and robustness of the optimizers in the context of building energy simulations. Main findings include the importance of tuning optimization algorithm parameters, consistently very fast convergence with the model-based optimizer RBFOpt but with a risk of being trapped in local optima, competitive results with classical metaheuristics such as a simple Genetic Algorithm and Particle Swarm Optimization Algorithm when tuned to specific problem dimensions, and consistently good results with CMA-ES when given a very large evaluation budget. It is striking however that no single algorithm dominates the benchmark for all considered performance metrics and/or optimization problems. Another important finding is that many of the discrete design variables in building energy optimization are in fact ordinal, which makes algorithm operators designed for continuous variables more effective than operators designed for combinatorial problems. These findings help users to select an appropriate optimizer for BEO problems and the available evaluation budget. Finally, this study provides an open-source framework for other researchers to benchmark their algorithms on the same test bed, hence enabling traceable and more rigorous performance comparisons.

Reliable data-driven models that estimate building envelope properties are indispensable for achieving emissions reduction targets. An extensive body of existing research investigates such methods, but benchmarking is limited and it is often unclear whether the approaches are scalable and robust to diverse building properties. Machine learning approaches, which natively handle complex, multivariate datasets, are rarely applied in this domain. This paper benchmarks seven different methods for characterization of the whole-building heat loss coefficient, including traditional gray box and novel black box approaches. To do so, a dataset of 16,000 simulated buildings is created. The models are benchmarked against ground truth, including an assessment of robustness to climate, construction materials, air-infiltration rate and occupant behaviour. We show the deep learning methods outperform other approaches in terms of accuracy and robustness, but that all of the approaches have limitations that restrain their practical usage. Based on this result, we suggest that further research is required to develop reliable and scalable approaches for the characterization of quantitative envelope properties from sensor data. The model code, data creation pipeline and final dataset used for this work are open-sourced so that future work can expand on this study. We encourage the use of our framework to support innovation in this domain.

This paper aims to integrate decentralized energy systems in a village in Switzerland, which has the goal to phase out fossil fuels and rely on local renewable energy sources. To reach this ambitious target, a revision of the current energy system is required together with retrofitting of the building stock. The method applied is based on the energy hub concept, which can be used to optimize the energy consumption during planning and operation. To apply the energy hub concept at neighbourhood level, a three step approach is required, including modelling the energy demand of the buildings, the evaluation of the local renewable potential, and the management and optimization of demand and supply. Centralized and decentralized local renewable sources are investigated, namely photovoltaics, biomass, or small hydro power. Scenarios are evaluated based on their environmental performance and savings of CO 2 emissions. Results show that an energy sustainability (ratio of energy demand covered by renewables) of 83% and a CO 2 emissions reduction of 86% can be achieved. The results further suggest that up to 50% of the available energy potential from renewables cannot be utilized in this community when insufficient storage is provided in the energy system.

Longwave (infrared) radiation is exchanged between exterior building surfaces, the ground and the sky. This process affects exterior surface temperatures and hence building energy consumption. Current approaches to modelling this exchange in building and microclimate analysis tools are discussed. There are various shortcomings, the most significant concerning radiation exchange between building surfaces at different temperatures. This work implements improvements to the longwave exchange processes in the EnergyPlus simulation engine, allowing improved calculation of surface temperatures and thus of heating and cooling loads. The improvements cover ground temperature assumptions and the coupling of other building surfaces. The coupling of radiative transfer was achieved by means of a timelag; it is shown that for small timesteps this approach is adequate. The new approaches are compared to the existing implementation in EnergyPlus for arrangements including a street canyon and shading devices. The combined impact gave an increase in surface temperature of up to 6 °C, with an average change of 2 °C. This caused a decrease in annual heating load of 18% and an increase in cooling load of 19%. This confirms the importance of accurate longwave radiation modelling both for buildings in dense urban areas and for buildings with external shading devices.