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The increasing density and height of residential buildings have heightened the concerns regarding inadequate sunshine exposure in outdoor areas. With multi-objective optimization experiments, existing evaluation measures, such as Sunshine Times (ST), are not able to sufficiently capture the spatial features required to find the best design for outdoor sunlight conditions quickly. Spatial Outdoor Sunshine Autonomy (sOSA) is proposed in this paper as a comprehensive indicator for the rapid assessment of cumulative sunlight exposure in various outdoor scenarios. The definition of the indicator is first presented through formula derivation. Subsequently, the rapid simulation method of the sOSA indicator in Rhino is demonstrated. Utilizing the Maigao Bridge Block in Nanjing as a prototype, the application of this indicator in multi-objective optimization experiments for new residential area planning and design is showcased. Finally, the results are evaluated, and the suggestion is made to combine sOSA with ST maps and wind speed cloud maps for enhanced practical guidance. • Current sunshine metrics lack spatial representation. • sOSA exhibits both spatiality and autonomy. • sOSA guides outdoor sunlight optimization swiftly through multi-objective optimization experiments. • Combining sOSA with other metrics enhances residential design guidance. [ABSTRACT FROM AUTHOR]

The competition for limited urban spaces due to the growing demand for both living space and green areas causes conflicts in urban development. This paper aims to provide urban planners with a systematic multi-criteria decision making process and a supporting tool-chain. The process uses multi-objective optimization (MOO) to quantify trade-offs, allowing planners to gain a broad perspective on optimal solutions. The generic process builds an urban simulation model, applies MOO, explores the results in terms of multi-criteria trade-offs, and guides the decision making of urban planners. To enable this process, we present a tool-chain for applying a Gaussian Process Regression based MOO algorithm to a computationally expensive urban simulation model. The tool-chain allows to identify Pareto-optimal solutions and their properties with reasonable computational effort. A case study model is set up in the Grasshopper environment and couples simulation components for outdoor thermal comfort and Life Cycle Assessment. It allows to identify multi-objective trade-offs for a high-dimensional space of urban configuration degrees of freedom such as outdoor vegetation, photovoltaics, and building characteristics. We compare the workflow results to other MOO algorithms and show how it can support decision making in urban planning at early design phases. In our case study, the tool chain was able to investigate the multi dimensional space of urban configurations. It systematically identified Pareto-optimal solutions therein and reduced the number of model evaluations significantly. The case study results showcase the trade-off between lifecycle-based global warming potential (GWP) and outdoor thermal comfort. We identified the number of trees and the coverage of the east and west façades with photovoltaics as the most important parameters. The proposed process proves to be a powerful multi-criteria decision support tool for urban planners. It allows to identify and quantify the Pareto Front of competing urban target trade-offs at early design phases. Additionally, it visualizes them according to the boundary conditions of urban development. The input configurations of the obtained Pareto-solutions serve as a base of urban planning recommendations. In our case study, trees and photovoltaics prove to be good levers in the area of GWP optimal solutions. However, urban planners need to carefully coordinate inputs when aiming for a specific trade-off balance. The tool-chain and the simulation model offer further potential for investigating neighborhood typologies. Thereby, applicants can derive scalable guidance to support the sustainable transformation of the urban environment. [Display omitted] • Structured process to explore the space of Pareto-optimal solutions in urban systems • Surrogate models allow to derive a continuous Pareto Front for decision making • First use of the Paref MOO algorithms in a computationally expensive urban simulation • Trade-off between lifecycle-based GWP and outdoor thermal comfort is quantified • Urban trees and building photovoltaics are important to steer this trade-off [ABSTRACT FROM AUTHOR]

In the last decade, many studies focused on finding optimal design solutions considering trade-offs between different aspects of building design. Accordingly, multi-objective optimization (MOO) approaches have been increasingly applied in the building industry. However, certain aspects must be deepened to ensure a more effective decision-making process in the early planning phase. On the one hand, uncertainties should be considered before making decisions to ensure the robustness of the optimal solutions; on the other hand, decisions are made at different times in building planning, and the sequential order of making decisions should be modeled. This paper presents a holistic two-stage multi-objective stochastic optimization (MOSO)-II framework to minimize the environmental impact of global warming potential (GWP) and cost throughout the entire life cycle of a building (phases of A1-A3, B4, B6 and C3-C4), considering passive and active design strategies in two consecutive stages, under uncertainty. Herein, individual/use and political/market uncertainties are considered. As a proof of concept, the proposed framework is applied in a case study for a generic zone in a multi-family terraced residential building type with solid brick construction. The advantages of the proposed framework are validated by comparing it with alternative single-stage MOSO frameworks. Results show that the proposed two-stage MOSO-II framework can deliver a smaller range of solutions with a better performance in terms of lower GWP and cost. It indicates that the proposed framework can effectively assist planners in decision-making by reducing the effort in choosing the proper solution. Secondly, the results also emphasize the importance of passive design strategies in sustainable building planning. In addition, the energy mix structure and cost of energy sources should be carefully adjusted in the future to promote a more ecologically sustainable building design. • A two-stage multi-objective stochastic optimization framework for building design. • Passive and active design strategies as two decision stages in building planning. • Targeting at minimum global warming potential and cost in the optimization problem. • Integration of uncertainties in the decision-making process for early design phases. [ABSTRACT FROM AUTHOR]

Predictive control is an effective method for addressing the increasing demand for heating, ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and efficiency. In this paper, the multi-objective problem of energy-efficient HVAC operation while tracking desired setpoints is addressed through an advanced control strategy, including a novel supervisory data-driven predictive control (DPC) and a local loop with a PI controller. The supervisory level is based on the integration of the DPC framework and the bilinear Koopman predictor. A deep neural network architecture is developed to realize the bilinear building thermal predictor. In addition, the integral input-to-state stability (iISS) constraint is enforced in the training procedure to obtain a global iISS control-oriented thermal predictor that enables the application of predictive control purely based on historical data. The effectiveness of the proposed approach is demonstrated through the simulation of a three-zone office in the transient system simulation software (TRNSYS). The proposed control configuration is implemented using a coupled TRNSYS-MATLAB simulation framework. It has shown significant potential for keeping the desired environment in line with energy conservation. Furthermore, a comparison is conducted between the proposed approach, the conventional controller, and another recent DPC approach, which demonstrates the suggested method's superiority. • Data-driven hierarchical control configuration for optimizing multi-zone HVAC systems. • Proposal of a DPC configuration using the Koopman operator and neural network. • Imposing iISS constraint on the predictor training algorithm. • Proposed method validation using the simulated three-zone HVAC system in TRNSYS. • Significant energy savings compared to the baseline controller and another DPC method. [Display omitted] [ABSTRACT FROM AUTHOR]