[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
[HTML][HTML] Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …
systems by extracting value from the data generated by the deployed metering and sensing …
CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production
Climate change is pushing an increasing number of nations to use green energy resources,
particularly solar power as an applicable substitute to traditional power sources. However …
particularly solar power as an applicable substitute to traditional power sources. However …
Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast
In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power
generation using a long short term memory (LSTM) neural network (NN). A synthetic …
generation using a long short term memory (LSTM) neural network (NN). A synthetic …
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 …
Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …
response in power systems. Electrical loads are characterized by volatility and uncertainty …
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 …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Deep learning in power systems research: A review
With the rapid growth of power systems measurements in terms of size and complexity,
discovering statistical patterns for a large variety of real-world applications such as …
discovering statistical patterns for a large variety of real-world applications such as …