Convolutional versus Dense Neural Networks: Comparing the Two Neural Networks Performance in Predicting Building Operational Energy Use Based on the Building Shape
Texas A&M University（德克萨斯A&M大学）
Farnaz Nazari, Wei Yan
备注：
The original paper was published in Building Simulation 2021 Conference Proceedings, IBPSA. Errata: the MSE values in the paper have been corrected to be unit free
A building self-shading shape impacts substantially on the amount of direct sunlight received by the building and contributes significantly to building operational energy use, in addition to other major contributing variables, such as materials and window-to-wall ratios. Deep Learning has the potential to assist designers and engineers by efficiently predicting building energy performance. This paper assesses the applicability of two different neural networks structures, Dense Neural Network (DNN) and Convolutional Neural Network (CNN), for predicting building operational energy use with respect to building shape. The comparison between the two neural networks shows that the DNN model surpasses the CNN model in performance, simplicity, and computation time. However, image-based CNN has the benefit of utilizing architectural graphics that facilitates design communication.
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Operational evaluation of data-driven forest fire forecasting models
CMCC Foundation - Euro-Mediterranean Center on Climate Change, Italy（CMCC基金会 - 欧洲-地中海气候变化中心，意大利）；Department of Engineering for Innovation, University of Salento, Italy（萨伦托大学创新工程系，意大利）
Predicting rice blast disease: machine learning versus process based models
IRIS Advanced Engineering（IRIS先进工程公司）；Hellenic Agricultural Organization-DEMETER（希腊农业组织-DEMETER）；GreenPowerMonitor（GreenPowerMonitor公司）；Università degli Studi di Milano（米兰大学）
A Latent Risk-Aware Machine Learning Approach for Predicting Operational Success in Clinical Trials based on TrialsBank
Groupe Sorintellis Inc.（索林特利斯集团）；Department of Computer Science and Operations Research, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）；Department of Computer Engineering, École Polytechnique de Montréal（蒙特利尔理工学院计算机工程系）；Faculty of Pharmacy, Université de Montréal（蒙特利尔大学药学院）；Faculty of Pharmacy, Université Laval（拉瓦尔大学药学院）；Vantage BioTrials（Vantage BioTrials公司）；CDS monitoring（CDS监测公司）；Department of Social and Preventive Medicine, School of Public Health, Université de Montréal（蒙特利尔大学公共卫生学院社会与预防医学系）；Department of Epidemiology, Biostatistics and Occupational Health, McGill University（麦吉尔大学流行病学、生物统计学与职业健康系）
Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance
Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139（土木与环境工程系，麻省理工学院，剑桥，马萨诸塞州，02139）；Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA 20139（交通运输与物流中心，麻省理工学院，剑桥，马萨诸塞州，20139）；Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 20139（城市研究与规划系，麻省理工学院，剑桥，马萨诸塞州，20139）
A Framework for Predicting Impactability of Healthcare Interventions Using Machine Learning Methods, Administrative Claims, Sociodemographic and App Generated Data
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Wellframe Inc.（Wellframe公司）
Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
University of Tennessee Graduate School（田纳西大学研究生院）
