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 …
State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …
operation because more precise forecasts mean reduced risk and improved stability and …
A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments
In the last few years, several countries have accomplished their determined renewable
energy targets to achieve their future energy requirements with the foremost aim to …
energy targets to achieve their future energy requirements with the foremost aim to …
A high-accuracy hybrid method for short-term wind power forecasting
In this article, a high-accuracy hybrid approach for short-term wind power forecasting is
proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data …
proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data …
Long-term wind power forecasting using tree-based learning algorithms
The intermittent and uncertain nature of wind places a premium on accurate wind power
forecasting for the reliable and efficient operation of power grids with large-scale wind power …
forecasting for the reliable and efficient operation of power grids with large-scale wind power …
A review of applications of artificial intelligent algorithms in wind farms
Wind farms are enormous and complex control systems. It is challenging and valuable to
control and optimize wind farms. Their applications are widely used in various industries …
control and optimize wind farms. Their applications are widely used in various industries …
Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models
M Zolfaghari, MR Golabi - Renewable Energy, 2021 - Elsevier
Electricity is an important pillar for the economic growth and the development of societies.
Surveying and predicting the electricity production (EP) is a valuable factor in the hands of …
Surveying and predicting the electricity production (EP) is a valuable factor in the hands of …
Better wind forecasting using evolutionary neural architecture search driven green deep learning
Climate Change heavily impacts global cities, the downsides of which can be minimized by
adopting renewables like wind energy. However, despite its advantages, the nonlinear …
adopting renewables like wind energy. However, despite its advantages, the nonlinear …
Optimal scheduling of electric vehicles charging in battery swapping station considering wind-photovoltaic accommodation
H Wang, H Ma, C Liu, W Wang - Electric Power Systems Research, 2021 - Elsevier
The disorderly charging of large-scale Electric Vehicles increases the peak-to-valley
difference of the grid and new energy absorption is facing difficulties. Considering these …
difference of the grid and new energy absorption is facing difficulties. Considering these …
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 …