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https://doi.org/10.2139/ssrn.4672061

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inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-extracted
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/978-3-031-37963-5_53openalex-extracted
A Comparison of LSTM and GRU Networks for Learning Symbolic Sequences
inferredcites →https://doi.org/10.1007/s42979-021-00592-xopenalex-extracted
Machine Learning: Algorithms, Real-World Applications and Research Directions
inferredcites →https://doi.org/10.1016/j.apenergy.2022.118793openalex-extracted
Resilient by design: Preventing wildfires and blackouts with microgrids
inferredcites →https://doi.org/10.1016/j.apenergy.2022.120089openalex-extracted
A novel short-term electrical load forecasting framework with intelligent feature engineering
inferredcites →https://doi.org/10.1016/j.apenergy.2023.120641openalex-extracted
Highly accurate peak and valley prediction short-term net load forecasting approach based on decomposition for power systems with high PV penetration
inferredcites →https://doi.org/10.1016/j.apenergy.2023.120716openalex-extracted
A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information
inferredcites →https://doi.org/10.1016/j.apenergy.2023.120920openalex-extracted
Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression
inferredcites →https://doi.org/10.1016/j.asoc.2019.105616openalex-extracted
Automatic time series analysis for electric load forecasting via support vector regression
inferredcites →https://doi.org/10.1016/j.egyr.2023.01.050openalex-extracted
TransformGraph: A novel short-term electricity net load forecasting model
inferredcites →https://doi.org/10.1016/j.enbuild.2021.111211openalex-extracted
Dual-stage attention-based long-short-term memory neural networks for energy demand prediction
inferredcites →https://doi.org/10.1016/j.enbuild.2023.113022openalex-extracted
Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system
inferredcites →https://doi.org/10.1016/j.energy.2021.122366openalex-extracted
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
inferredcites →https://doi.org/10.1016/j.epsr.2021.107173openalex-extracted
Convolutional and recurrent neural network based model for short-term load forecasting
inferredcites →https://doi.org/10.1016/j.epsr.2023.109415openalex-extracted
A Study on the development of long-term hybrid electrical load forecasting model based on MLP and statistics using massive actual data considering field applications
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.jnlssr.2020.06.009openalex-extracted
Impact of Australia's catastrophic 2019/20 bushfire season on communities and environment. Retrospective analysis and current trends
inferredcites →https://doi.org/10.1016/j.rineng.2023.101296openalex-extracted
A new approach to seasonal energy consumption forecasting using temporal convolutional networks
inferredcites →https://doi.org/10.1073/pnas.1613193114openalex-extracted
Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States
inferredcites →https://doi.org/10.1109/eait.2018.8470406openalex-extracted
Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster
inferredcites →https://doi.org/10.1109/icei52466.2021.00020openalex-extracted
A Novel Forecasting Method for Short-term Load based on TCN-GRU Model
inferredcites →https://doi.org/10.1109/icmlc.2012.6358885openalex-extracted
Random forest based ensemble system for short term load forecasting
inferredcites →https://doi.org/10.1109/isgtasia49270.2021.9715272openalex-extracted
A Short-Term Load Forecasting Technique Using Extreme Gradient Boosting Algorithm
inferredcites →https://doi.org/10.1109/tpwrs.2020.3028133openalex-extracted
Short-Term Load Forecasting for Industrial Customers Based on TCN-LightGBM
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/10.1186/s40537-020-00305-wopenalex-extracted
Survey on categorical data for neural networks
inferredcites →https://doi.org/10.20944/preprints202103.0216.v1openalex-extracted
inferredcites →https://doi.org/10.3115/v1/d14-1179openalex-extracted
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.2211.14730openalex-extracted
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
inferredcites →https://doi.org/10.5281/zenodo.5754302openalex-extracted
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