Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
A review of deep learning for renewable energy forecasting
As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …
improving the accuracy of renewable energy forecasting is critical to power system planning …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
Advanced methods for photovoltaic output power forecasting: A review
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into
the grid. The design of accurate photovoltaic output forecasters remains a challenging issue …
the grid. The design of accurate photovoltaic output forecasters remains a challenging issue …
Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning
The outputs of photovoltaic (PV) power are random and uncertain due to the variations of
meteorological elements, which may disturb the safety and stability of power system …
meteorological elements, which may disturb the safety and stability of power system …
[HTML][HTML] Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution
Accurate forecasts of the power production of distributed photovoltaic (PV) systems are
essential to support grid operation and enable a high PV penetration rate in the electricity …
essential to support grid operation and enable a high PV penetration rate in the electricity …
Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting
With the fast expansion of renewable energy system installed capacity in recent years, the
availability, stability, and quality of smart grids have become increasingly important. The …
availability, stability, and quality of smart grids have become increasingly important. The …
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution …
Develo** an accurate and robust prediction of long-term average global solar irradiation
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
An effective hybrid NARX-LSTM model for point and interval PV power forecasting
This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
Solar forecasting with hourly updated numerical weather prediction
Solar forecasters have hitherto been restricting the application of numerical weather
prediction (NWP) to day-ahead forecasting scenarios. With the hourly updated NWP models …
prediction (NWP) to day-ahead forecasting scenarios. With the hourly updated NWP models …