Machine learning methods for solar radiation forecasting: A review
Forecasting the output power of solar systems is required for the good operation of the
power grid or for the optimal management of the energy fluxes occurring into the solar …
power grid or for the optimal management of the energy fluxes occurring into the solar …
How solar radiation forecasting impacts the utilization of solar energy: A critical review
N Krishnan, KR Kumar, CS Inda - Journal of Cleaner Production, 2023 - Elsevier
The demand for energy generation from solar energy resource has been exponentially
increasing in recent years. It is integral for a grid operator to maintain the balance between …
increasing in recent years. It is integral for a grid operator to maintain the balance between …
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 …
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station
Non-predictive or inaccurate weather forecasting can severely impact the community of
users such as farmers. Numerical weather prediction models run in major weather …
users such as farmers. Numerical weather prediction models run in major weather …
Near real-time wind speed forecast model with bidirectional LSTM networks
Wind is an important source of renewable energy, often used to provide clean electricity to
remote areas. For optimal extraction of this energy source, there is a need for an accurate …
remote areas. For optimal extraction of this energy source, there is a need for an accurate …
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 …
Deep learning-based effective fine-grained weather forecasting model
It is well-known that numerical weather prediction (NWP) models require considerable
computer power to solve complex mathematical equations to obtain a forecast based on …
computer power to solve complex mathematical equations to obtain a forecast based on …
A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea
Renewable and sustainable energy systems and policies have globally been promoted to
transition from fossil fuel sources to environmentally friendly renewable energy sources such …
transition from fossil fuel sources to environmentally friendly renewable energy sources such …
Wind power prediction using deep neural network based meta regression and transfer learning
An innovative short term wind power prediction system is proposed which exploits the
learning ability of deep neural network based ensemble technique and the concept of …
learning ability of deep neural network based ensemble technique and the concept of …