A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
[HTML][HTML] Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain
Wind power forecasting has supported operational decision-making for power system and
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
A novel genetic LSTM model for wind power forecast
Variations of produced power in windmills may influence the appropriate integration in
power-driven grids which may disrupt the balance between electricity demand and its …
power-driven grids which may disrupt the balance between electricity demand and its …
Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM
Offshore wind power is one of the fastest-growing energy sources worldwide, which is
environmentally friendly and economically competitive. Short-term time series wind speed …
environmentally friendly and economically competitive. Short-term time series wind speed …
[HTML][HTML] Renewable Energy Forecasting Based on Stacking Ensemble Model and Al-Biruni Earth Radius Optimization Algorithm
AA Alghamdi, A Ibrahim, ESM El-Kenawy… - Energies, 2023 - mdpi.com
Introduction: Wind speed and solar radiation are two of the most well-known and widely
used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples …
used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples …
Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
In this paper, an efficient new hybrid time series forecasting model combining variational
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
A review and discussion of decomposition-based hybrid models for wind energy forecasting applications
Z Qian, Y Pei, H Zareipour, N Chen - Applied energy, 2019 - Elsevier
With the continuous growth of wind power integration into the electrical grid, accurate wind
power forecasting is an important component in management and operation of power …
power forecasting is an important component in management and operation of power …
Spatio-temporal graph deep neural network for short-term wind speed forecasting
M Khodayar, J Wang - IEEE Transactions on Sustainable …, 2018 - ieeexplore.ieee.org
Wind speed forecasting is still a challenge due to the stochastic and highly varying
characteristics of wind. In this paper, a graph deep learning model is proposed to learn the …
characteristics of wind. In this paper, a graph deep learning model is proposed to learn the …
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian …
M Sharifzadeh, A Sikinioti-Lock, N Shah - Renewable and Sustainable …, 2019 - Elsevier
Renewable energy from wind and solar resources can contribute significantly to the
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
YY Hong, CLPP Rioflorido - Applied Energy, 2019 - Elsevier
Wind power generation is always associated with uncertainties as a result of fluctuations of
wind speed. Accurate predictions of wind power generation are important for the efficient …
wind speed. Accurate predictions of wind power generation are important for the efficient …