Research graph
References from Edge-optimized and interpretable deep learning framework for high-volatility residential load prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
Control and optimisation of power grids using smart meter data: A review
10.3390/s23042118 · 2023 · External reference
Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China
10.1016/j.jup.2017.01.004 · 2017 · External reference
10.1016/j.egyai.2020.100002 · 2020 · Admitted local publication
Performance analysis of smart metering for smart grid: An overview
10.1016/j.rser.2015.04.170 · 2015 · External reference
Review of artificial intelligence techniques in green/smart buildings
10.1016/j.suscom.2023.100861 · 2023 · External reference
Introducing a novel approach in one-step ahead energy load forecasting
10.1016/j.suscom.2021.100616 · 2021 · External reference
A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction
2023 · External reference
Pattern-driven behaviour for demand-side management: an analysis of appliance use
10.1016/j.enbuild.2024.113988 · 2024 · External reference
Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics
10.1016/j.apenergy.2022.120023 · 2022 · External reference
A systematic review on power systems planning and operations management with grid integration of transportation electrification at scale
10.1016/j.adapen.2023.100147 · 2023 · External reference
Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach
10.1016/j.apenergy.2021.117335 · 2021 · External reference
Short-term residential load forecasting: impact of calendar effects and forecast granularity
10.1016/j.apenergy.2017.07.114 · 2017 · External reference
Spatiotemporal feature encoded deep learning method for rooftop PV potential assessment
10.1016/j.apenergy.2025.126171 · 2025 · External reference
A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants
10.1016/j.energy.2021.120026 · 2021 · External reference
Deep neural network battery life and voltage prediction by using data of one cycle only
10.1016/j.apenergy.2021.118134 · 2022 · External reference
Time series forecasting using a hybrid ARIMA and neural network model
10.1016/s0925-2312(01)00702-0 · 2003 · External reference
A kalman filter-based bottom-up approach for household short-term load forecast
10.1016/j.apenergy.2019.05.102 · 2019 · External reference
A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing
10.1016/j.apenergy.2021.116808 · 2021 · External reference
Forecasting energy use in buildings using artificial neural networks: A review
10.3390/en12173254 · 2019 · External reference
Building energy prediction using artificial neural networks: A literature survey
10.1016/j.enbuild.2021.111718 · 2022 · External reference
Approximating support vector machine with artificial neural network for fast prediction
10.1016/j.eswa.2014.02.025 · 2014 · External reference
Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network
2020 · External reference
The artificial intelligence black box and the failure of intent and causation
2018 · External reference
Time series forecasting with multi-headed attention-based deep learning for residential energy consumption
10.3390/en13184722 · 2020 · External reference
Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques
10.1016/j.jobe.2021.103406 · 2022 · External reference
Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks
10.1016/j.enbuild.2021.111647 · 2022 · External reference
Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network
10.1016/j.physd.2019.132306 · 2020 · External reference
Predicting energy consumption using LSTM, multi-layer GRU and drop-GRU neural networks
10.3390/s22114062 · 2022 · External reference
Predicting household electric power consumption using multi-step time series with convolutional LSTM
10.1016/j.bdr.2022.100360 · 2023 · External reference
Development of an LSTM-based model for energy consumption prediction with data pre-analysis
2021 · External reference
Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory
10.1016/j.adapen.2020.100004 · 2021 · External reference
Short-term residential load forecasting based on LSTM recurrent neural network
10.1109/tsg.2017.2753802 · 2019 · External reference
Short-term load forecasting model of GRU network based on deep learning framework
2018 · External reference
LSTM and GRU neural network performance comparison study: Taking yelp review dataset as an example
2020 · External reference
A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation
10.1109/tii.2021.3056867 · 2021 · External reference
Machine learning optimization model for reducing the electricity loads in residential energy forecasting
10.1016/j.suscom.2023.100876 · 2023 · External reference
Improving electric energy consumption prediction using CNN and Bi-LSTM
10.3390/app9204237 · 2019 · External reference
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
10.1016/j.enconman.2017.10.008 · 2017 · External reference
Conceptual understanding of convolutional neural network- a deep learning approach
10.1016/j.procs.2018.05.069 · 2018 · External reference
Review of image classification algorithms based on convolutional neural networks
10.3390/rs13224712 · 2021 · External reference
A hybrid deep learning model to estimate the future electricity demand of sustainable cities
10.3390/su16156503 · 2024 · External reference
Enhancing short-term load forecasting with a CNN-GRU hybrid model: A comparative analysis
10.1109/access.2024.3511653 · 2024 · External reference
A hybrid deep learning and clonal selection algorithm-based model for commercial building energy consumption prediction
10.1177/00368504241283360 · 2024 · External reference
Forecasting of electrical energy consumption using hybrid models of gru, cnn, lstm, and ml regressors
2025 · External reference
Learning global and local features of power load series through transformer and 2D-CNN: An image-based multi-step forecasting approach incorporating phase space reconstruction
2024 · External reference
A performance degradation prediction model for PEMFC based on bi-directional long short-term memory and multi-head self-attention mechanism
10.1016/j.ijhydene.2024.02.181 · 2024 · External reference
A deep learning method based on CNN-[BiGRU] and attention mechanism for proton exchange membrane fuel cell performance degradation prediction
10.1016/j.ijhydene.2024.11.127 · 2024 · External reference
Reducing the consumption of household systems using hybrid deep learning techniques
10.1016/j.suscom.2023.100874 · 2023 · External reference
Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units
10.1080/15567036.2022.2097751 · 2022 · External reference
Predicting photovoltaic power generation using double-layer bidirectional long short-term memory-convolutional network
10.1007/s40095-022-00530-4 · 2023 · External reference
Soft measurement and prediction of refrigerant leakage based on SVR-LSTM
10.1016/j.ijrefrig.2023.04.021 · 2023 · External reference
Clustering of load profiles of residential customers using extreme points and demographic characteristics
10.3390/electronics10030290 · 2021 · External reference
K-means clustering of electricity consumers using time-domain features from smart meter data
2023 · External reference
Photovoltaic power forecasting with a long short-term memory autoencoder networks
10.1007/s00500-023-08497-y · 2023 · External reference
A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior
10.1016/j.apenergy.2021.118197 · 2022 · External reference
Survey of feature selection and extraction techniques for stock market prediction
10.1186/s40854-022-00441-7 · 2023 · External reference
Well production forecasting based on ARIMA-LSTM model considering manual operations
10.1016/j.energy.2020.119708 · 2021 · External reference
A study on the prediction of book borrowing based on ARIMA-SVR model
10.1016/j.procs.2021.05.057 · 2021 · External reference
Explainable multi-fidelity Bayesian neural network for distribution system state estimation
10.1016/j.apenergy.2025.125972 · 2025 · External reference
Explainable artificial intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities
10.1016/j.egyai.2022.100169 · 2022 · External reference
Explainable multi-step heating load forecasting: using SHAP values and temporal attention mechanisms for enhanced interpretability
10.1016/j.egyai.2025.100480 · 2025 · External reference
Short-term wind power forecasting based on dual attention mechanism and gated recurrent unit neural network
10.3389/fenrg.2024.1346000 · 2024 · External reference
A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning
10.1016/j.compeleceng.2023.109063 · 2024 · External reference
A modified energy demand forecasting model using hybrid CNN-LSTM with transformer for univariate time series
2025 · External reference
A systematic review for transformer-based long-term series forecasting
10.1007/s10462-024-11044-2 · 2025 · External reference
An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge
10.1016/j.adapen.2023.100142 · 2023 · External reference
Unresolved reference
2025 · External reference
It’s different: Insights into home energy consumption in India
2013 · External reference
A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm
10.1016/j.apenergy.2024.124620 · 2025 · External reference
A state-of-the-art comparative review of load forecasting methods: characteristics, perspectives, and applications
10.1016/j.ecmx.2025.100922 · 2025 · External reference
Artificial intelligence and machine learning for distributed energy resource management systems: applications, frameworks, and future directions
10.1016/j.apenergy.2025.127109 · 2026 · External reference
Nonlinearity in the relationships between urban form and residential energy use intensity
10.1016/j.apenergy.2025.125344 · 2025 · External reference
An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study
10.1038/sdata.2016.122 · 2017 · External reference
Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014
10.1038/sdata.2016.37 · 2016 · External reference
ENERTALK: A national-scale dataset of electricity consumption from Korean households
10.1038/s41597-019-0212-5 · 2019 · External reference
