Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …
potential to change our energy supply, trade, and consumption dramatically. The new …
[HTML][HTML] Distributed energy systems: A review of classification, technologies, applications, and policies
The sustainable energy transition taking place in the 21st century requires a major
revamping of the energy sector. Improvements are required not only in terms of the …
revamping of the energy sector. Improvements are required not only in terms of the …
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
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 …
Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM
More and more photovoltaic (PV) power generation is incorporated into the grid. However,
the intermittence and fluctuation of solar energy have brought huge challenges to the safe …
the intermittence and fluctuation of solar energy have brought huge challenges to the safe …
[HTML][HTML] A review and taxonomy of wind and solar energy forecasting methods based on deep learning
G Alkhayat, R Mehmood - Energy and AI, 2021 - Elsevier
Renewable energy is essential for planet sustainability. Renewable energy output
forecasting has a significant impact on making decisions related to operating and managing …
forecasting has a significant impact on making decisions related to operating and managing …
Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction …
J Qu, Z Qian, Y Pei - Energy, 2021 - Elsevier
Accurate forecasting of photovoltaic power plays a pivotal role in the integration, operation,
and scheduling of smart grid systems. Notably, volatility and intermittence of solar energy …
and scheduling of smart grid systems. Notably, volatility and intermittence of solar energy …
A hybrid deep learning model for short-term PV power forecasting
P Li, K Zhou, X Lu, S Yang - Applied Energy, 2020 - Elsevier
The integration of PV power brings great economic and environmental benefits. However,
the high penetration of PV power may challenge the planning and operation of the existing …
the high penetration of PV power may challenge the planning and operation of the existing …
A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network
K Wang, X Qi, H Liu - Applied Energy, 2019 - Elsevier
Accurate photovoltaic power forecasting is of great help to the operation of photovoltaic
power generation system. However, due to the instability, intermittence, and randomness of …
power generation system. However, due to the instability, intermittence, and randomness of …
State of the art of machine learning models in energy systems, a systematic review
Machine learning (ML) models have been widely used in the modeling, design and
prediction in energy systems. During the past two decades, there has been a dramatic …
prediction in energy systems. During the past two decades, there has been a dramatic …