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Review of Deep Learning Methods for Individual Households’ Energy Consumption Forecasting

https://doi.org/10.2139/ssrn.4808151
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A Novel Deep Learning Architecture Based IoT Time-Series for Energy Consumption Forecasting in Smart Households
inferredcites →https://doi.org/10.1016/j.apenergy.2018.10.078openalex-extracted
Probabilistic individual load forecasting using pinball loss guided LSTM
inferredcites →https://doi.org/10.1016/j.apenergy.2020.116177openalex-extracted
Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network
inferredcites →https://doi.org/10.1016/j.apenergy.2021.116509openalex-extracted
Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features
inferredcites →https://doi.org/10.1016/j.apenergy.2021.118197openalex-extracted
A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior
inferredcites →https://doi.org/10.1016/j.energy.2019.05.230openalex-extracted
Predicting residential energy consumption using CNN-LSTM neural networks
inferredcites →https://doi.org/10.1016/j.energy.2019.116324openalex-extracted
Deep ensemble learning based probabilistic load forecasting in smart grids
inferredcites →https://doi.org/10.1016/j.energy.2021.120480openalex-extracted
Electrical load-temperature CNN for residential load forecasting
inferredcites →https://doi.org/10.1016/j.energy.2021.120682openalex-extracted
Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
inferredcites →https://doi.org/10.1016/j.engappai.2022.105358openalex-extracted
Short-term residential load forecasting using Graph Convolutional Recurrent Neural Networks
inferredcites →https://doi.org/10.1016/j.epsr.2020.106733openalex-extracted
Multiple households very short-term load forecasting using bayesian networks
inferredcites →https://doi.org/10.1016/j.epsr.2022.108152openalex-extracted
A CNN-Sequence-to-Sequence network with attention for residential short-term load forecasting
inferredcites →https://doi.org/10.1016/j.ijforecast.2013.08.002openalex-extracted
A new error measure for forecasts of household-level, high resolution electrical energy consumption
inferredcites →https://doi.org/10.1016/j.ijforecast.2015.11.011openalex-extracted
Probabilistic electric load forecasting: A tutorial review
inferredcites →https://doi.org/10.1016/j.jpdc.2022.01.012openalex-extracted
Collaborative deep learning framework on IoT data with bidirectional NLSTM neural networks for energy consumption forecasting
inferredcites →https://doi.org/10.1016/j.scs.2021.103484openalex-extracted
MultiCycleNet: Multiple Cycles Self-Boosted Neural Network for Short-term Electric Household Load Forecasting
inferredcites →https://doi.org/10.1038/sdata.2015.7openalex-extracted
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
inferredcites →https://doi.org/10.1049/cit2.12060openalex-extracted
Deep learning for time series forecasting: The electric load case
inferredcites →https://doi.org/10.1109/access.2019.2949065openalex-extracted
A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households
inferredcites →https://doi.org/10.1109/access.2019.2963045openalex-extracted
Short-Term Prediction of Residential Power Energy Consumption via CNN and Multi-Layer Bi-Directional LSTM Networks
inferredcites →https://doi.org/10.1109/access.2020.2976500openalex-extracted
Denoising Autoencoder-Based Missing Value Imputation for Smart Meters
inferredcites →https://doi.org/10.1109/access.2020.3009537openalex-extracted
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
inferredcites →https://doi.org/10.1109/access.2020.3028281openalex-extracted
Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting
inferredcites →https://doi.org/10.1109/access.2021.3053069openalex-extracted
A Pyramid-CNN Based Deep Learning Model for Power Load Forecasting of Similar-Profile Energy Customers Based on Clustering
inferredcites →https://doi.org/10.1109/access.2021.3053317openalex-extracted
Individualized Short-Term Electric Load Forecasting With Deep Neural Network Based Transfer Learning and Meta Learning
inferredcites →https://doi.org/10.1109/access.2021.3061370openalex-extracted
Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model
inferredcites →https://doi.org/10.1109/access.2021.3094089openalex-extracted
Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach
inferredcites →https://doi.org/10.1109/access.2021.3095420openalex-extracted
Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA
inferredcites →https://doi.org/10.1109/access.2022.3140818openalex-extracted
Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers
inferredcites →https://doi.org/10.1109/aupec.2017.8282478openalex-extracted
Effect of automatic hyperparameter tuning for residential load forecasting via deep learning
inferredcites →https://doi.org/10.1109/icc40277.2020.9148937openalex-extracted
Electrical Load Forecasting Using Edge Computing and Federated Learning
inferredcites →https://doi.org/10.1109/isgt-asia.2019.8881343openalex-extracted
Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis
inferredcites →https://doi.org/10.1109/oajpe.2020.3029979openalex-extracted
Energy Forecasting: A Review and Outlook
inferredcites →https://doi.org/10.1109/pmaps.2018.8440559openalex-extracted
Probabilistic Forecasting of Household Electrical Load Using Artificial Neural Networks
inferredcites →https://doi.org/10.1109/ptc.2019.8810899openalex-extracted
Deep Learning Based Forecasting of Individual Residential Loads Using Recurrence Plots
inferredcites →https://doi.org/10.1109/tii.2019.2942353openalex-extracted
Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids
inferredcites →https://doi.org/10.1109/tsg.2017.2686012openalex-extracted
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
inferredcites →https://doi.org/10.1109/tsg.2017.2753802openalex-extracted
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
inferredcites →https://doi.org/10.1109/tsg.2018.2818167openalex-extracted
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
inferredcites →https://doi.org/10.1109/tsg.2021.3091469openalex-extracted
Hybrid Multitask Multi-Information Fusion Deep Learning for Household Short-Term Load Forecasting
inferredcites →https://doi.org/10.1155/2021/6661798openalex-extracted
Household Electricity Load Forecasting Based on Multitask Convolutional Neural Network with Profile Encoding
inferredcites →https://doi.org/10.1177/0739456x17723971openalex-extracted
Guidance on Conducting a Systematic Literature Review
inferredcites →https://doi.org/10.1186/s42162-021-00148-6openalex-extracted
A scoping review of deep neural networks for electric load forecasting
inferredcites →https://doi.org/10.3390/electronics11142189openalex-extracted
Interpretable LSTM Based on Mixture Attention Mechanism for Multi-Step Residential Load Forecasting
inferredcites →https://doi.org/10.3390/electronics9010068openalex-extracted
Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques
inferredcites →https://doi.org/10.3390/en11010224openalex-extracted
Topology-Based Estimation of Missing Smart Meter Readings
inferredcites →https://doi.org/10.3390/en11051138openalex-extracted
Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks
inferredcites →https://doi.org/10.3390/en12040739openalex-extracted
Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder
inferredcites →https://doi.org/10.3390/en13102672openalex-extracted
HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning
inferredcites →https://doi.org/10.3390/en13184722openalex-extracted
Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption
inferredcites →https://doi.org/10.3390/smartcities4010014openalex-extracted
Transfer Learning by Similarity Centred Architecture Evolution for Multiple Residential Load Forecasting
inferredcites →https://doi.org/10.3390/su9081484openalex-extracted
The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-extracted
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.5255/ukda-sn-7857-2openalex-extracted
Low Carbon London Project: Data from the Dynamic Time-of-Use Electricity Pricing Trial, 2013
inferredcites →https://doi.org/10.5772/63631openalex-extracted
Advanced Metering Infrastructure Based on Smart Meters in Smart Grid
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