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建筑模拟（英文版）
建筑模拟（英文版）

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1996-3599

建筑模拟（英文版）/Journal Building SimulationCSCD北大核心EISCI
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    TSILNet:A novel hybrid model for energy disaggregation based on two-stage improved TCN combined with IECA-LSTM

    Ziwei ZhuMengran ZhouFeng HuKun Wang...
    2083-2095页
    查看更多>>摘要：Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and planning.However,existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow,which affects the energy disaggregation accuracy.To this end,this paper designs a method based on temporal convolutional network(TCN),efficient channel attention(ECA),and long short-term memory(LSTM).The method first creatively proposes a two-stage improved TCN(TSTCN),which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load features.Then a novel improved ECA attention mechanism(IECA)is embedded,which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information fusion.Finally,the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load disaggregation.Experiments on two real-world datasets,REDD and UK-DALE,show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating power.The results show that the mean absolute error(MAE)of all appliances decreases by 18.67％on average,and the F1 score improves by 38.70％.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Evolving multi-objective optimization framework for early-stage building design:Improving energy efficiency,daylighting,view quality,and thermal comfort

    Lingrui LiZongxin QiQingsong MaWeijun Gao...
    2097-2123页
    查看更多>>摘要：Computational performance-driven design optimization(CPDDO)informs early building design decisions,enhancing projects'responsiveness to local climates.This paper reviews recent CPDDO studies,identifies prevalent gaps,and proposes a refined optimization framework.The framework stands out by:(1)integrating view quality alongside energy,daylight,and thermal comfort considerations,with a vector-simulation-based metric considering content,access and clarity;(2)incorporating users'adaptive behavior patterns in simulations;and(3)employing a hybrid weighting method to accommodate diverse project demands and support robust design decisions.This study applies the framework to optimize the shape and facade variables of a medium-sized office building in Guangzhou,Chongqing,Qingdao,Lanzhou,and Changchun,representing hot,warm,mixed,cool,and cold climates,respectively.Results highlight that geometry features(aspect ratio,orientation,window-to-wall ratio(WWR),and shading devices),as well as window and blinds constructions significantly impact energy,daylight,thermal comfort and view quality.Different climatic conditions,objective priorities,and facade orientations necessitate tailored design variables.Furthermore,certain findings challenge conventional recommendations;for instance,buildings in colder climates benefit from increased WWR,due to enhanced potential to harness solar radiation and improved view access,while high-performance envelope thermal settings mitigate heat transfer.These findings underscore the need for detailed,targeted research in early-stage design.The developed CPDDO framework proves effective and user-friendly,offering new possibilities for optimizing building performance,thus holds the potential to foster green,comfortable,and sustainable architecture in various practical applications.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Developing an integrated prediction model for daylighting,thermal comfort,and energy consumption in residential buildings based on the stacking ensemble learning algorithm

    Hainan YanGuohua JiShuqi CaoBaihui Zhang...
    2125-2143页
    查看更多>>摘要：Accurate and rapid predictions of residential building performance are crucial for both new building designs and existing building renovations.This study develops an integrated prediction model using a stacking ensemble learning algorithm to predict daylighting,thermal comfort,and energy consumption in residential buildings.The model incorporates multimodal residential building information as inputs,including image-based floorplans and vector-based building parameters.A comparative analysis is presented to evaluate the prediction performance of the proposed stacking ensemble learning algorithm against three base models:Resnet-50,Inception-V4,and Vision Transformer(ViT-32).The results indicated that the stacking ensemble learning algorithm outperforms the base models,reducing the mean absolute percentage error(MAPE)by 0.17％-1.94％and the coefficient of variation root mean square error(CV-RMSE)by 0.37％-2.06％for daylighting metrics;the MAPE by 0.63％-4.46％and the CV-RMSE by 0.62％-5.13％for thermal comfort metrics;the MAPE by 1.42％-6.43％and the CV-RMSE by 0.27％-5.09％for energy consumption metrics of the testing dataset.Further prediction error analyses also indicate that the stacking ensemble learning algorithm consistently yields smaller prediction errors across all performance metrics compared to the three base models.In addition,this study compares the stacking ensemble learning algorithm to traditional machine learning models in terms of prediction accuracy,robustness,and generalization ability,highlighting the advantages of the stacking ensemble learning algorithm with image-based inputs.The proposed stacking ensemble learning algorithm demonstrates superior accuracy,stability,and generalizability,offering valuable and practical design support for building design and renovation processes.
      原文链接:
    • NETL
    • NSTL
    • 万方数据