Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model
Y Ding, Y Dang - Energy, 2023 - Elsevier
Accurate prediction of renewable energy generation can provide a reference for
policymakers to formulate energy development strategies. However, it is difficult to predict …
policymakers to formulate energy development strategies. However, it is difficult to predict …
Multivariate time series forecasting with GARCH models on graphs
Data that house topological information is manifested as relationships between multiple
variables via a graph formulation. Various methods have been developed for analyzing time …
variables via a graph formulation. Various methods have been developed for analyzing time …
Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm
X He, B He, T Qin, C Lin, J Yang - Energy, 2024 - Elsevier
The large-scale integration of wind power to the grid poses some potential challenges to the
power system. Accurate wind power forecasts reduce the impact of the nonlinearities and …
power system. Accurate wind power forecasts reduce the impact of the nonlinearities and …
Modelling the industrial production of electric and gas utilities through the model
This work aims to extend previous research on how a trifactorial stochastic model, which we
call CIR 3, can be turned into a forecasting tool for energy time series. In particular, in this …
call CIR 3, can be turned into a forecasting tool for energy time series. In particular, in this …
A novel structure adaptive discrete grey Bernoulli model and its application in renewable energy power generation prediction
Y Wang, R Yang, L Sun - Expert Systems with Applications, 2024 - Elsevier
Currently, the renewable energy power generation industry has entered a new stage, and
accurate renewable energy power generation prediction is of great significance for the …
accurate renewable energy power generation prediction is of great significance for the …
A big data approach for demand response management in smart grid using the prophet model
Smart Grids (SG) generate extensive data sets regarding the system variables, viz., and
demand and supply. These extremely large data sets are known as big data. Hence …
demand and supply. These extremely large data sets are known as big data. Hence …
Wind Power Deviation Charge Reduction using Machine Learning
High penetration of wind power plants in power systems resulted in various challenges such
as frequent system imbalances due to highly uncertain and variable wind generation …
as frequent system imbalances due to highly uncertain and variable wind generation …
[HTML][HTML] Wind power deviation charge reduction using long short term memory network
High penetration of variable generation like wind in modern power systems results in
frequent load-generation imbalances. Additional spinning reserves and balancing services …
frequent load-generation imbalances. Additional spinning reserves and balancing services …
Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
H Song, Q Chen, T Jiang, Y Li, X Li, W **… - arxiv preprint arxiv …, 2025 - arxiv.org
Accurately predicting the wind power output of a wind farm across various time scales
utilizing Wind Power Forecasting (WPF) is a critical issue in wind power trading and …
utilizing Wind Power Forecasting (WPF) is a critical issue in wind power trading and …
Comparison Among ARIMA, ANN, and SVR Models for Wind Power Deviation Charge Reduction
Adverse climatic changes and exponentially increasing electric power demand have been
forcing various countries to increase the percentage share of renewable generation in their …
forcing various countries to increase the percentage share of renewable generation in their …