A neural network-based surrogate model to predict building features from heating and cooling load signatures

IF 2.2 4区 工程技术 Q2 CONSTRUCTION & BUILDING TECHNOLOGY
Shane Ferreira, B. Gunay, Adam Wills, Farzeen Rizvi
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引用次数: 0
基于神经网络的代用模型,根据冷热负荷特征预测建筑物特征
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来源期刊
Journal of Building Performance Simulation
Journal of Building Performance Simulation CONSTRUCTION & BUILDING TECHNOLOGY-
CiteScore
5.50
自引率
12.00%
发文量
55
审稿时长
12 months
期刊介绍: The Journal of Building Performance Simulation (JBPS) aims to make a substantial and lasting contribution to the international building community by supporting our authors and the high-quality, original research they submit. The journal also offers a forum for original review papers and researched case studies We welcome building performance simulation contributions that explore the following topics related to buildings and communities: -Theoretical aspects related to modelling and simulating the physical processes (thermal, air flow, moisture, lighting, acoustics). -Theoretical aspects related to modelling and simulating conventional and innovative energy conversion, storage, distribution, and control systems. -Theoretical aspects related to occupants, weather data, and other boundary conditions. -Methods and algorithms for optimizing the performance of buildings and communities and the systems which service them, including interaction with the electrical grid. -Uncertainty, sensitivity analysis, and calibration. -Methods and algorithms for validating models and for verifying solution methods and tools. -Development and validation of controls-oriented models that are appropriate for model predictive control and/or automated fault detection and diagnostics. -Techniques for educating and training tool users. -Software development techniques and interoperability issues with direct applicability to building performance simulation. -Case studies involving the application of building performance simulation for any stage of the design, construction, commissioning, operation, or management of buildings and the systems which service them are welcomed if they include validation or aspects that make a novel contribution to the knowledge base.
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