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Enhanced Long-Term Prediction Based on 2d Tensorization for Building Energy Consumption

https://doi.org/10.2139/ssrn.4606538
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A review of nonlinear FFT-based computational homogenization methods
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On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning
inferredcites →https://doi.org/10.1016/j.apenergy.2018.12.042openalex-extracted
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
inferredcites →https://doi.org/10.1016/j.apenergy.2019.03.078openalex-extracted
Vector field-based support vector regression for building energy consumption prediction
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114499openalex-extracted
Statistical investigations of transfer learning-based methodology for short-term building energy predictions
inferredcites →https://doi.org/10.1016/j.apenergy.2022.120635openalex-extracted
Anomaly detection based on joint spatio-temporal learning for building electricity consumption
inferredcites →https://doi.org/10.1016/j.apenergy.2023.121244openalex-extracted
Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions
inferredcites →https://doi.org/10.1016/j.egyr.2022.01.162openalex-extracted
Physical energy and data-driven models in building energy prediction: A review
inferredcites →https://doi.org/10.1016/j.enbuild.2021.111478openalex-extracted
Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings
inferredcites →https://doi.org/10.1016/j.energy.2018.05.155openalex-extracted
Intelligent techniques for forecasting electricity consumption of buildings
inferredcites →https://doi.org/10.1016/j.energy.2020.119208openalex-extracted
A hybrid deep transfer learning strategy for short term cross-building energy prediction
inferredcites →https://doi.org/10.1016/j.epsr.2019.106073openalex-extracted
Load demand forecasting of residential buildings using a deep learning model
inferredcites →https://doi.org/10.1016/j.rser.2017.09.108openalex-extracted
A review of data-driven approaches for prediction and classification of building energy consumption
inferredcites →https://doi.org/10.1016/j.rser.2020.110287openalex-extracted
Machine learning applications in urban building energy performance forecasting: A systematic review
inferredcites →https://doi.org/10.1109/access.2019.2925740openalex-extracted
Forecasting the Short-Term Electricity Consumption of Building Using a Novel Ensemble Model
inferredcites →https://doi.org/10.1109/bigcomp48618.2020.000-8openalex-extracted
Forecasting Building Electricity Power Consumption Using Deep Learning Approach
inferredcites →https://doi.org/10.1109/cac.2018.8623540openalex-extracted
A Building Energy Consumption Prediction Method Based on Random Forest and ARMA
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-extracted
Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2017.634openalex-extracted
Aggregated Residual Transformations for Deep Neural Networks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00986openalex-extracted
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
inferredcites →https://doi.org/10.1109/iciot.2019.00029openalex-extracted
Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach
inferredcites →https://doi.org/10.1371/journal.pone.0264586openalex-extracted
Fruit classification using attention-based MobileNetV2 for industrial applications
inferredcites →https://doi.org/10.1609/aaai.v35i12.17325openalex-extracted
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
inferredcites →https://doi.org/10.3390/app11073194openalex-extracted
Online Forecasting and Anomaly Detection Based on the ARIMA Model
inferredcites →https://doi.org/10.3390/en14030608openalex-extracted
A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings
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