Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives
In the last few years, methods falling within the family of randomization-based machine
learning models have grasped a great interest in the Artificial Intelligence community, mainly …
learning models have grasped a great interest in the Artificial Intelligence community, mainly …
Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks
Spatio-temporal wind power forecasting is significant to the stability of electric power
systems. However, the accuracy of power forecasting results is easily impaired by the …
systems. However, the accuracy of power forecasting results is easily impaired by the …
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …
renewable energy resources. Wind energy is attracting attention worldwide due to its …
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
The use of wind energy plays a vital role in society owing to its economic and environmental
importance. Knowing the wind power generation within a specific time window is useful for …
importance. Knowing the wind power generation within a specific time window is useful for …
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 hybrid model for deep learning short-term power load forecasting based on feature extraction statistics techniques
GF Fan, YY Han, JW Li, LL Peng, YH Yeh… - Expert Systems with …, 2024 - Elsevier
Accurate and reliable load forecasting can ensure the safety and economy of power system
operation. To improve the accuracy of short-term power load forecasting, this paper adopts …
operation. To improve the accuracy of short-term power load forecasting, this paper adopts …
A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting
Accurate wind power forecasting is essential for (i) the management of wind energy,(ii)
increasing the integration of generated power into the electrical grid, and (iii) enhancing …
increasing the integration of generated power into the electrical grid, and (iii) enhancing …
Dynamic ensemble deep echo state network for significant wave height forecasting
Forecasts of the wave heights can assist in the data-driven control of wave energy systems.
However, the dynamic properties and extreme fluctuations of the historical observations …
However, the dynamic properties and extreme fluctuations of the historical observations …
Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems
L Yin, J **e - Applied Energy, 2021 - Elsevier
With the advancement of power market reform, accurate load forecasting can ensure the
stable operation of power systems increasingly. The randomness of feature change such as …
stable operation of power systems increasingly. The randomness of feature change such as …