Research graph
References from Residential Electrical Load, Solar Energy and Electricity Bill Forecasting Using Hybrid Machine Learning Models with Time-of-Use Tariffs: A Case Study of Durban, South Africa. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1007/978-981-97-9626-7_16
10.1007/978-981-97-9626-7_16 · External reference
Optimization of renewable energy based hybrid energy system using evolutionary computational techniques
10.1007/s40866-025-00245-5 · 2025 · External reference
Design and techno-economic assessment of a standalone photovoltaic-diesel-battery hybrid energy system for electrification of rural areas: A step towards sustainable development
10.1016/j.renene.2024.120556 · 2024 · External reference
10.3390/en18195243
10.3390/en18195243 · External reference
10.3390/en19051174
10.3390/en19051174 · External reference
Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration
10.1186/s44147-024-00428-4 · 2024 · External reference
Enhancing Electric Grid Flexibility for the Integration of Variable Renewable Energy: Challenges, Innovations, and Future Directions
10.1109/access.2026.3660837 · 2026 · External reference
Electrical load and solar power forecasting using machine learning techniques
10.1007/s44444-025-00012-y · 2025 · External reference
A deep learning approach for fairness-based time of use tariff design
10.1016/j.enpol.2024.114230 · 2024 · External reference
Machine learning for renewable energy forecasting: Advances, challenges, and future directions
10.1186/s40807-026-00242-x · 2026 · External reference
Time-series and deep learning approaches for renewable energy forecasting in Dhaka: A comparative study of ARIMA, SARIMA, and LSTM models
10.1007/s43621-025-01733-5 · 2025 · External reference
Artificial intelligence and classical statistical models for time series forecasting: A comprehensive review
10.1186/s40537-025-01318-z · 2025 · External reference
10.3390/atmos16091056
10.3390/atmos16091056 · External reference
Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data
10.1016/j.apenergy.2024.122709 · 2024 · External reference
Deep learning-based regional electricity demand prediction using smart meter data in Sri Lanka
10.1109/access.2026.3677106 · 2026 · External reference
Toward grid-interactive and low-carbon buildings: A comparative analysis of artificial intelligence-driven optimization of renewable sizing and demand-side control
2026 · External reference
Empirical mode decomposition with random forest model based short term load forecasting
2022 · External reference
10.3390/en17184681
10.3390/en17184681 · External reference
10.3390/su16062328
10.3390/su16062328 · External reference
A novel probabilistic gradient boosting model with multi-approach feature selection and iterative seasonal trend decomposition for short-term load forecasting
10.1016/j.energy.2024.130975 · 2024 · External reference
10.3390/pr12112466
10.3390/pr12112466 · External reference
10.3390/en17215304
10.3390/en17215304 · External reference
Deep belief rule based photovoltaic power forecasting method with interpretability
10.1038/s41598-022-18820-6 · 2022 · External reference
Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction
10.1186/s42162-024-00340-4 · 2024 · External reference
10.3390/electronics13061079
10.3390/electronics13061079 · External reference
Random vector functional link neural network based ensemble deep learning
10.1016/j.patcog.2021.107978 · 2021 · External reference
A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets
10.1016/j.apenergy.2023.122413 · 2023 · External reference
10.3389/fenrg.2024.1490152
10.3389/fenrg.2024.1490152 · External reference
Enhanced renewable power and load forecasting using RF-XGBoost stacked ensemble
10.1007/s00202-024-02273-3 · 2024 · External reference
Nowcasting the next hour of residential load using boosting ensemble machines
10.1038/s41598-025-91767-6 · 2025 · External reference
A transfer learning-based hybrid model with LightGBM for smart grid short-term energy load prediction
10.1177/01445987241256472 · 2024 · External reference
A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model
10.1016/j.apenergy.2024.124166 · 2024 · External reference
Machine learning long-term electricity demand forecasting system for strategic energy investments
10.1038/s41598-026-45123-x · 2026 · External reference
10.3390/a19020114
10.3390/a19020114 · External reference
Short-term load forecasting in smart grids using hybrid deep learning
10.1109/access.2024.3358182 · 2024 · External reference
10.1007/978-3-031-94386-7_2
10.1007/978-3-031-94386-7_2 · External reference
10.3390/en17081926
10.3390/en17081926 · External reference
Improving energy management practices through accurate building energy consumption prediction: Analyzing the performance of LightGBM, RF and XGBoost models with advanced optimization strategies
10.1007/s00202-025-03167-8 · 2025 · External reference
Forecasting global sustainable energy from renewable sources using random forest algorithm
10.1016/j.rineng.2024.103789 · 2025 · External reference
A time series forecasting approach based on gradient boosting method for IoT-based solar energy production systems
10.1016/j.enss.2025.04.003 · 2025 · External reference
A stacked gradient boosting–XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids
10.1038/s41598-026-47042-3 · 2026 · External reference
Gradient boosted bagging for evolving data stream regression
10.1007/s10618-025-01147-x · 2025 · External reference
10.3390/en18061518
10.3390/en18061518 · External reference
Load forecasting for energy communities: A novel LSTM-XGBoost hybrid model based on smart meter data
10.1186/s42162-022-00212-9 · 2022 · External reference
10.3390/systems12070254
10.3390/systems12070254 · External reference
A deep belief network-based energy consumption prediction model for water source heat pump system
10.1016/j.applthermaleng.2024.124000 · 2024 · External reference
10.1007/978-3-032-05159-2_6
10.1007/978-3-032-05159-2_6 · External reference
Short-term load estimation based on improved DBN-LSTM
10.1186/s40807-025-00192-w · 2025 · External reference
10.3390/systems14030257
10.3390/systems14030257 · External reference
10.3390/electronics14050887
10.3390/electronics14050887 · External reference
10.3389/fenrg.2024.1393794
10.3389/fenrg.2024.1393794 · External reference
10.3390/en19020453
10.3390/en19020453 · External reference
Ensemble deep learning and machine learning: Applications, opportunities, challenges, and future directions
2024 · External reference
Ensemble deep learning techniques for time series analysis: A comprehensive review, applications, open issues, challenges, and future directions
10.1007/s10586-024-04684-0 · 2025 · External reference
10.3390/su16104069
10.3390/su16104069 · External reference
Comparative analysis of model evaluation metrics in energy systems, environmental modeling, and sustainability science
10.1155/er/6170467 · 2026 · External reference
Bias calibration and error propagation adjustment for ml-based time series forecasting: A systematic study for pjm’s electricity load forecast amid virginia’s data center surge
10.1016/j.energy.2025.138411 · 2025 · External reference
Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: A case study
10.1016/j.rser.2017.04.073 · 2017 · External reference
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10.3390/math13162694
10.3390/math13162694 · External reference
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10.1007/978-3-031-94386-7_2
10.1007/978-3-031-94386-7_2 · ExternalCitation · doi-reference
10.1007/978-3-032-05159-2_6
10.1007/978-3-032-05159-2_6 · ExternalCitation · doi-reference
10.1007/978-981-97-9626-7_16
10.1007/978-981-97-9626-7_16 · ExternalCitation · doi-reference
Enhanced renewable power and load forecasting using RF-XGBoost stacked ensemble
10.1007/s00202-024-02273-3 · ExternalCitation · doi-reference
Improving energy management practices through accurate building energy consumption prediction: Analyzing the performance of LightGBM, RF and XGBoost models with advanced optimization strategies
10.1007/s00202-025-03167-8 · ExternalCitation · doi-reference
Ensemble deep learning techniques for time series analysis: A comprehensive review, applications, open issues, challenges, and future directions
10.1007/s10586-024-04684-0 · ExternalCitation · doi-reference
Gradient boosted bagging for evolving data stream regression
10.1007/s10618-025-01147-x · ExternalCitation · doi-reference
Optimization of renewable energy based hybrid energy system using evolutionary computational techniques
10.1007/s40866-025-00245-5 · ExternalCitation · doi-reference
Time-series and deep learning approaches for renewable energy forecasting in Dhaka: A comparative study of ARIMA, SARIMA, and LSTM models
10.1007/s43621-025-01733-5 · ExternalCitation · doi-reference
Electrical load and solar power forecasting using machine learning techniques
10.1007/s44444-025-00012-y · ExternalCitation · doi-reference
A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets
10.1016/j.apenergy.2023.122413 · ExternalCitation · doi-reference
Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data
10.1016/j.apenergy.2024.122709 · ExternalCitation · doi-reference
A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model
10.1016/j.apenergy.2024.124166 · ExternalCitation · doi-reference
A deep belief network-based energy consumption prediction model for water source heat pump system
10.1016/j.applthermaleng.2024.124000 · ExternalCitation · doi-reference
A novel probabilistic gradient boosting model with multi-approach feature selection and iterative seasonal trend decomposition for short-term load forecasting
10.1016/j.energy.2024.130975 · ExternalCitation · doi-reference
Bias calibration and error propagation adjustment for ml-based time series forecasting: A systematic study for pjm’s electricity load forecast amid virginia’s data center surge
10.1016/j.energy.2025.138411 · ExternalCitation · doi-reference
A deep learning approach for fairness-based time of use tariff design
10.1016/j.enpol.2024.114230 · ExternalCitation · doi-reference
A time series forecasting approach based on gradient boosting method for IoT-based solar energy production systems
10.1016/j.enss.2025.04.003 · ExternalCitation · doi-reference
Random vector functional link neural network based ensemble deep learning
10.1016/j.patcog.2021.107978 · ExternalCitation · doi-reference
Design and techno-economic assessment of a standalone photovoltaic-diesel-battery hybrid energy system for electrification of rural areas: A step towards sustainable development
10.1016/j.renene.2024.120556 · ExternalCitation · doi-reference
Forecasting global sustainable energy from renewable sources using random forest algorithm
10.1016/j.rineng.2024.103789 · ExternalCitation · doi-reference
Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: A case study
10.1016/j.rser.2017.04.073 · ExternalCitation · doi-reference
Deep belief rule based photovoltaic power forecasting method with interpretability
10.1038/s41598-022-18820-6 · ExternalCitation · doi-reference
Nowcasting the next hour of residential load using boosting ensemble machines
10.1038/s41598-025-91767-6 · ExternalCitation · doi-reference
Machine learning long-term electricity demand forecasting system for strategic energy investments
10.1038/s41598-026-45123-x · ExternalCitation · doi-reference
A stacked gradient boosting–XGBoost ensemble with ridge meta-learner for accurate short-term solar PV power forecasting in smart grids
10.1038/s41598-026-47042-3 · ExternalCitation · doi-reference
Short-term load forecasting in smart grids using hybrid deep learning
10.1109/access.2024.3358182 · ExternalCitation · doi-reference
Enhancing Electric Grid Flexibility for the Integration of Variable Renewable Energy: Challenges, Innovations, and Future Directions
10.1109/access.2026.3660837 · ExternalCitation · doi-reference
Deep learning-based regional electricity demand prediction using smart meter data in Sri Lanka
10.1109/access.2026.3677106 · ExternalCitation · doi-reference
Comparative analysis of model evaluation metrics in energy systems, environmental modeling, and sustainability science
10.1155/er/6170467 · ExternalCitation · doi-reference
A transfer learning-based hybrid model with LightGBM for smart grid short-term energy load prediction
10.1177/01445987241256472 · ExternalCitation · doi-reference
Artificial intelligence and classical statistical models for time series forecasting: A comprehensive review
10.1186/s40537-025-01318-z · ExternalCitation · doi-reference
Short-term load estimation based on improved DBN-LSTM
10.1186/s40807-025-00192-w · ExternalCitation · doi-reference
Machine learning for renewable energy forecasting: Advances, challenges, and future directions
10.1186/s40807-026-00242-x · ExternalCitation · doi-reference
Load forecasting for energy communities: A novel LSTM-XGBoost hybrid model based on smart meter data
10.1186/s42162-022-00212-9 · ExternalCitation · doi-reference
Application of improved DBN and GRU based on intelligent optimization algorithm in power load identification and prediction
10.1186/s42162-024-00340-4 · ExternalCitation · doi-reference
Integrated encoder-decoder-based wide and deep convolution neural networks strategy for electricity theft arbitration
10.1186/s44147-024-00428-4 · ExternalCitation · doi-reference
10.3389/fenrg.2024.1393794
10.3389/fenrg.2024.1393794 · ExternalCitation · doi-reference
10.3389/fenrg.2024.1490152
10.3389/fenrg.2024.1490152 · ExternalCitation · doi-reference
10.3390/a19020114
10.3390/a19020114 · ExternalCitation · doi-reference
10.3390/atmos16091056
10.3390/atmos16091056 · ExternalCitation · doi-reference
10.3390/electronics13061079
10.3390/electronics13061079 · ExternalCitation · doi-reference
10.3390/electronics14050887
10.3390/electronics14050887 · ExternalCitation · doi-reference
10.3390/en17081926
10.3390/en17081926 · ExternalCitation · doi-reference
10.3390/en17184681
10.3390/en17184681 · ExternalCitation · doi-reference
10.3390/en17215304
10.3390/en17215304 · ExternalCitation · doi-reference
10.3390/en18061518
10.3390/en18061518 · ExternalCitation · doi-reference
10.3390/en18195243
10.3390/en18195243 · ExternalCitation · doi-reference
10.3390/en19020453
10.3390/en19020453 · ExternalCitation · doi-reference
10.3390/en19051174
10.3390/en19051174 · ExternalCitation · doi-reference
10.3390/math13162694
10.3390/math13162694 · ExternalCitation · doi-reference
10.3390/pr12112466
10.3390/pr12112466 · ExternalCitation · doi-reference
10.3390/su16062328
10.3390/su16062328 · ExternalCitation · doi-reference
10.3390/su16104069
10.3390/su16104069 · ExternalCitation · doi-reference
10.3390/systems12070254
10.3390/systems12070254 · ExternalCitation · doi-reference
10.3390/systems14030257
10.3390/systems14030257 · ExternalCitation · doi-reference
