Combining physics-based and data-driven modeling for building energy systems

This publication has 30 references indexed in Scilit:

• Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models
  Applied Energy, 2023
• Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies
  Advances in Applied Energy, 2023
• A hybrid-model forecasting framework for reducing the building energy performance gap
  Advanced Engineering Informatics, 2022
• Physics informed neural networks for control oriented thermal modeling of buildings
  Applied Energy, 2022
• Benchmarking cooling and heating energy demands considering climate change, population growth and cooling device uptake
  Applied Energy, 2021
• Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones
  Applied Energy, 2020
• ANN‐based energy consumption prediction model up to 2050 for a residential building: Towards sustainable decision making
  Environmental Progress & Sustainable Energy, 2020
• A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses
  Applied Energy, 2019
• Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow
  Applied Energy, 2018
• Constructing large scale surrogate models from big data and artificial intelligence
  Applied Energy, 2017