Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented.

Urban building energy modeling – A review of a nascent field

Optimal scheduling of integrated demand response-enabled integrated energy systems with uncertain renewable generations: A Stackelberg game approach

A review on multi-objective optimization framework in wind energy forecasting techniques and applications

As one of the most important and advanced technology for carbon-mitigation in the building sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multi-disciplinary approaches, the researchers, as well as tool developers and practitioners, are facing opportunities and challenges during the application of BEM at multiple scales and stages, e.g., building/system/community levels and planning/design/operation stages. By reviewing recent studies, this paper aims to provide a clear picture of how BEM performs in solving different research questions on varied scales of building phase and spatial resolution, with a focus on the objectives and frameworks, modelling methods and tools, applicability and transferability. To guide future applications of BEM for performance-driven building energy management, we classified the current research trends and future research opportunities into five topics that span through different stages and levels: (1) Simulation for performance-driven design for new building and retrofit design, (2) Model-based operational performance optimization, (3) Integrated simulation using data measurements for digital twin, (4) Building simulation supporting urban energy planning, and (5) Modelling of building-to-grid interaction for demand response. Additionally, future research recommendations are discussed, covering potential applications of BEM through integration with occupancy and behaviour modelling, integration with machine learning, quantification of model uncertainties, and linking to building monitoring systems. 

Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies

Multi-objective optimization design of green building envelopes and air conditioning systems for energy conservation and CO2 emission reduction

Combined multi-objective optimization and robustness analysis framework for building integrated energy system under uncertainty

Prioritizing urban planning factors on community energy performance based on GIS-informed building energy modeling

Uncertainty complicates the optimization model of distributed energy systems, whereas it is favorable to address the fragility of optimal solutions. This work presents a two-stage stochastic programming model to optimize the economic and environmental objective simultaneously, considering the uncertainties including energy demands and renewable energy resources. Meanwhile, the single-objective optimization is combined with the epsilon-constraint method in order to obtain the Pareto frontier, and then the decision making method is introduced to identify the tradeoff solution considering the both objectives. The results shows that, compared with the deterministic model, the stochastic model leads to an underestimation of the total cost, but a similarity of carbon emission. In addition, the computational cost of the stochastic model is significantly higher than that of the deterministic model, which means that the designers need to take their computational resources into account when modeling DES under uncertainty.

Multi-Objective Optimization of Distributed Energy Systems Under Uncertainty

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Combined multi-objective optimization and robustness analysis framework for building integrated energy system under uncertainty

Prioritizing urban planning factors on community energy performance based on GIS-informed building energy modeling

Multi-Objective Optimization of Distributed Energy Systems Under Uncertainty