Physics-Informed Deep Learning for Power Consumption Prediction in Smart Homes

This publication has 19 references indexed in Scilit:

• Long-term electrical energy demand forecasting by using artificial intelligence/machine learning techniques
  Electrical Engineering, 2024
• PPINN: Parareal physics-informed neural network for time-dependent PDEs
  Computer Methods in Applied Mechanics and Engineering, 2020
• Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics
  Neural Networks, 2020
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
  Computer Methods in Applied Mechanics and Engineering, 2020
• Adversarial uncertainty quantification in physics-informed neural networks
  Journal of Computational Physics, 2019
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  Journal of Computational Physics, 2019
• Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
  Applied Energy, 2018
• Prediction of appliances energy use in smart homes
  Energy, 2012
• Day‐ahead price forecasting of electricity markets by a hybrid intelligent system
  European Transactions on Electrical Power, 2008
• On the approximate realization of continuous mappings by neural networks
  Neural Networks, 1989