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Reducing energy consumption in buildings presents a challenge for the construction and architectural industries. Stakeholders in the building sector require innovative products and systems to reduce energy usage effectively. Building Energy Simulation (BES) tools are essential for understanding energy-related issues during the design phase. However, the existing BES tools are often complex and costly, making them inaccessible to many architects and engineers who lack the software expertise for integrating new systems into existing Building Energy Simulation frameworks. To address this gap, the authors of this article have developed a new tool that enables early-stage evaluation of building performance. Additionally, the tool includes Water Flow Glazing (WFG) as a construction element that is part of both the facade and the building’s heating and cooling system. The authors validated the methodology by comparing the results from the new tool with those from the commercial BES tool Indoor Climate and Energy IDA-ICE 5.0 in accordance with ASHRAE standards. The same cases were tested by comparing the indoor temperature of a room with the power absorbed by the water, as measured by both tools. A WFG facade can effectively help maintain comfortable room temperatures throughout both winter and summer while producing renewable thermal energy via water heat absorption. The accuracy of this tool was validated using the normalized root mean square error between results from the new tool and those from IDA-ICE 5.0, which remained below the maximum allowable error established by ASHRAE. Validation of the tool using an experimental prototype showed that a coefficient of determination (R2) of 0.91 can be achieved through iterative refinement between the model and measured data.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10–100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.

\"Energy modeling calculations for urban, complex buildings are most effective during the early design phase. And most analysis takes only four to sixteen hours to get results you can use. This software-agnostic book, which is intended for you to use as a professional architect, shows you how to reduce the energy use of all buildings. Written by a practicing architect who specializes in energy modeling, the book includes case studies of net-zero buildings, of Living Building Challenge-certified buildings, as well as of projects with less lofty goals to demonstrate how energy simulation has helped designers make early decisions. Within each case study, author Kjell Anderson mentions the software used and other software that could have been used to get similar results so that you learn general concepts without being tied to particular programs. Each chapter builds on the theory from previous chapters, includes a summary of concept-level hand calculations (if applicable), and gives comprehensive explanations with examples. Topics covered include comfort, design energy simulation, climate analysis, master planning, conceptual design, design development, and existing buildings so that you can create more responsive designs quicker\"-- Provided by publisher.

The existing non-residential building stock can generally be considered energy-inefficient. The ECUK 2017 report states that the final energy consumption for commercial buildings remained static. The 2010 recast Energy Performance Building Directive (EPBD) has set out a requirement for commercial and residential buildings to be nearly Zero Energy Buildings (nZEBs) by 2020. Despite this, within the UK, a definition does not exist at the national level for commercial nZEBs (new or existing). This paper utilises the EU zebra2020 data tool to set a standard based on the existing UK nZEB commercial building stock. The aim of this paper is to investigate and assess the potential of various energy-efficient measures (EEMs) and their contribution to reducing energy consumption, primary energy consumption (PEC), and CO2 emissions whilst taking into consideration the energy and cost savings of those measures. The analysis is carried out using Thermal Analysis Simulation software (Tas, Edsl). The model validation obtained a performance gap of less than 5%. The results show that it is possible to achieve the nZEB standard for older UK hotel buildings if several measures are implemented and the initial selection of EEMs is carefully investigated. Based on the results, reaching the nZEB target should first take into consideration improving the building fabric and/or building envelope elements to lower the energy demand. Once the energy demand of the building is lowered, the incorporation of a renewable/microgeneration system is essential to meeting the nZEB target.

With the development and popularization of the Internet of Things, big data, cloud computing, and other technologies, Digital twin technology (DTT) is increasingly applied to building operation and maintenance (O&M). However, most of the research focuses on building energy consumption, safety, and other management, and relatively little research on the monitoring of building terminal energy using information. The information is closely related to occupants’ behavior, such as air conditioning, lighting, shading, window status information, as well as personnel number and location, and it has a great impact on building energy consumption. Due to different occupants’ behaviors, the level of building energy consumption varies several times or even more. Take an office building as an example. Based on digital twin technology, the framework of building energy using intelligent monitoring is constructed. It mainly includes four parts, namely building physical space, virtual twin space, predictive control simulation engine, and twin big data. For each part, functions are realized through building Information Modelling (BIM), smart sensors, and Internet of Things (IoT) technologies. Based on the standard framework and every function realization method, the DTT can used for building O&M effectually. The application of building an intelligent control system based on the occupants’ characteristics is simulated and analyzed in Designbuilder software 6.1.2. The results show that the digital technology application in building intelligent control systems can realize maximum energy saving for 30%. However, the DTT in building O&M is not widely used now. There is a lot of research to be completed in the future.

Under the influence of global epidemics and the need for urban expansion, many outpatient buildings have been rapidly constructed, but the problem of high energy consumption has been neglected. There is a lack of research on the impact of outpatient building forms on energy consumption in different climate zones. Many studies have demonstrated that the energy consumption of a given building can be greatly reduced by adopting a reasonable spatial form design at the early stages of design. Therefore, if architects choose a reasonable spatial form, this could effectively reduce energy consumption. In this study, outpatient building cases in China were summarized, and three typical spatial forms were proposed: the centralized, corridor, and courtyard forms. The DesignBuilder tool was used to simulate and analyse the typical building energy consumption in different climate zones. The results showed that the corridor form (southwards) should be chosen in the severe cold zone, the centralized form (southwards) should be chosen in the cold zone and the hot summer and cold winter zone, the centralized form (northwards) should be chosen in the hot summer and warm winter zone, and the centralized or corridor form can be chosen in the warm zone. The results of this study could provide a reference for energy-efficient design of outpatient buildings in China and other regions with similar conditions and could help architects quickly select reasonable spatial forms at the early stages of design.

Well-designed building envelope components are essential in addressing global warming. Fenestration and shading system (F&SS) not only promote energy conservation and emission reduction but also enhance occupant satisfaction by improving indoor environments. However, existing research often prioritizes energy use, daylight, and thermal comfort while neglecting view quality, a factor closely tied to mental health and productivity. This study employs multi-objective optimization (MOO) to balance energy consumption, view quality, daylight, and thermal comfort in office buildings located in hot-humid climates. By optimizing variables such as window-to-wall ratio (WWR) and shading device dimensions, the research integrates random forest models with SHapley Additive exPlanations (SHAP) analysis to quantify the influence of design parameters on optimization goals. Results indicate maximum improvements of 25.62% in energy use intensity (EUI), 23.18% in thermal comfort percentage (TCP), and 37.96% in useful daylight illuminance (UDI), highlighting the substantial potential of the proposed framework. This research refines the MOO framework for F&SS design, offering new insights into view quality considerations. Recommended values, such as a WWR of 0.6, provide practical guidance for architects in balancing energy efficiency and occupant comfort.

In order to attain an efficient path into the enhancement of the sustainable conservation and management of energy resources in the buildings and residential sections, efforts by all relevant institutions in the area of energy are essential and primitive. Hence, the development of the energy supply model in the building and the provision of fundamental solutions in this area is rudimentary. The key to any program related to this matter is to boost and extend energy efficiency and energy resource management. According to the global statistics and processed data, the residential sector accounts for 34 percent of the total energy consumption in Iran, which is the highest energy consumption compared to all existing sectors; therefore, immeasurable research has been done on optimizing energy usage in the building sector. One of the most efficient energy optimization methods can be achieved by using energy storage materials in the outer walls of the buildings. In this project, a research‐based residential building is considered as a case to investigate for energy optimization. In the first stage, the building is simulated and the energy consumption of the building is measured. Afterward, with actual measurements, real consumption is approximated and compared with the results of the software. After verifying the software data, the insulation is simulated in the software and the outcomes of using this insulator are investigated to reduce energy consumption. Energy storage materials are considered as a type of phase‐change material. These materials have the same characteristics which is having a different melting point and, by capturing and losing heat at design temperatures, they can help maintain energy inside the building that is how they can be considered as an insulator. Within the appropriate software which is Design‐Builder, miscellaneous types of these materials are available with various features. The final results point out that the use of this material saves up to 30% of the heating energy and about 8% in the cooling load of the building. In this project, a research‐based residential building is considered as a case to investigate for energy optimization. In the first stage, the building is simulated and the energy consumption of the building is measured. Afterward, with actual measurements, real consumption is approximated and compared with the results of the software. After verifying the software data, the insulation is simulated in the software and the outcomes of using this insulator are investigated to reduce energy consumption.