[HTML][HTML] Trends and gaps in photovoltaic power forecasting with machine learning
The share of solar energy in the electricity mix increases year after year. Knowing the
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
production of photovoltaic (PV) power at each instant of time is crucial for its integration into …
[HTML][HTML] Hybrid energy system integration and management for solar energy: A review
The conventional grid is increasingly integrating renewable energy sources like solar
energy to lower carbon emissions and other greenhouse gases. While energy management …
energy to lower carbon emissions and other greenhouse gases. While energy management …
[HTML][HTML] Deakin microgrid digital twin and analysis of AI models for power generation prediction
To achieve carbon neutral by 2025, Deakin University launched a AUD 23 million
Renewable Energy Microgrid in 2020 with a 7-megawatt solar farm, the largest at an …
Renewable Energy Microgrid in 2020 with a 7-megawatt solar farm, the largest at an …
Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines
This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in
predicting solar PV output using one-year data from three solar PV installations in the …
predicting solar PV output using one-year data from three solar PV installations in the …
[HTML][HTML] A three-step weather data approach in solar energy prediction using machine learning
Solar energy plays a critical part in lowering CO 2 emissions and other greenhouse gases
when integrated into the grid. Higher solar energy penetration is hindered by its …
when integrated into the grid. Higher solar energy penetration is hindered by its …
Enhancing solar forecasting accuracy with sequential deep artificial neural network and hybrid random forest and gradient boosting models across varied terrains
Effective solar energy utilization demands improvements in forecasting due to the
unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two …
unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two …
Accuracy assessment of satellite-based and reanalysis solar irradiance data for solar PV output forecasting using SARIMAX
Forecasting models are often constrained by data availability, and in forecasting solar
photovoltaic (PV) output, the literature suggests that solar irradiance contributes the most to …
photovoltaic (PV) output, the literature suggests that solar irradiance contributes the most to …
Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region
Solar power forecasting is substantial for the utilization, planning, and designing of solar
power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role …
power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role …
A power regulation strategy for heat pipe cooled reactors based on deep learning and hybrid data-driven optimization algorithm
M Huang, C Peng, DU Zhengyu, Y Liu - Energy, 2024 - Elsevier
Heat pipe cooled reactors are ideal for use in remote or isolated locations as dependable,
small-scale power sources, thanks to their excellent design characteristics. To tackle real …
small-scale power sources, thanks to their excellent design characteristics. To tackle real …
Comparison of CLOT-Adjusted AHI-8/9 and FY-4A Solar Irradiance Products for Solar PV Power Output Forecasting Using LSTM
Accurate solar photovoltaic power output (PPV) forecasting in grid planning is crucial to
ensure sufficient power supply during peak hours and periods with high electricity demand …
ensure sufficient power supply during peak hours and periods with high electricity demand …