Deep learning in smart grid technology: A review of recent advancements and future prospects
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …
a promising landscape for high grid reliability and efficient energy management. This …
Review of deterministic and probabilistic wind power forecasting: Models, methods, and future research
IK Bazionis, PS Georgilakis - Electricity, 2021 - mdpi.com
The need to turn to more environmentally friendly sources of energy has led energy systems
to focus on renewable sources of energy. Wind power has been a widely used source of …
to focus on renewable sources of energy. Wind power has been a widely used source of …
Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model
The stability operation and real-time control of the integrated energy system with distributed
energy resources determines the higher and higher requirements for the accuracy of solar …
energy resources determines the higher and higher requirements for the accuracy of solar …
Solar irradiance forecasting based on direct explainable neural network
As the penetration of solar energy into electrical power and energy system expands in
recent years over the world, accurate solar irradiance forecasting is becoming highly …
recent years over the world, accurate solar irradiance forecasting is becoming highly …
Short-term wind power interval prediction method using VMD-RFG and Att-GRU
With the increasing penetration of wind energy, accurate wind power prediction is essential
for efficient utilization, equipment protection, and stable grid-connection of wind energy …
for efficient utilization, equipment protection, and stable grid-connection of wind energy …
Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks
D Wei, J Wang, X Niu, Z Li - Applied Energy, 2021 - Elsevier
Deep recurrent neural networks, such as gated recurrent units and long short-term
memories, have been widely applied in wind speed forecasting. However, the simulations of …
memories, have been widely applied in wind speed forecasting. However, the simulations of …
Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method
Probabilistic wind power forecasting includes more detailed information than deterministic
forecasting, which can provide reliable guidance for the optimal decisions of power system …
forecasting, which can provide reliable guidance for the optimal decisions of power system …
An innovative interpretable combined learning model for wind speed forecasting
P Du, D Yang, Y Li, J Wang - Applied Energy, 2024 - Elsevier
Wind energy is taken as one of the most potential green energy sources, whose accurate
and stable prediction is important to improve the efficiency of wind turbines as well as to …
and stable prediction is important to improve the efficiency of wind turbines as well as to …
Machine learning approaches to predict electricity production from renewable energy sources
Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system
Abstract In this paper, Machine Learning (ML) based techniques known as Support Vector
Regression (SVR) and Gradient Boosting Regression Trees (GBRT) are utilized for …
Regression (SVR) and Gradient Boosting Regression Trees (GBRT) are utilized for …