A review of wind speed and wind power forecasting with deep neural networks
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …
has attracted increasing attention. However, intermittent electricity generation resulting from …
Bayesian CNN-BiLSTM and vine-GMCM based probabilistic forecasting of hour-ahead wind farm power outputs
The importance of the accurate forecasting of power outputsof wind-based generation
systems is increasing, as their contributions to the total system generation are rising …
systems is increasing, as their contributions to the total system generation are rising …
Data-driven and knowledge-guided denoising diffusion model for flood forecasting
Data-driven models have been successfully applied in hydrological fields such as flood
forecasting. However, limitations to the solutions to scientific problems still exist in this field …
forecasting. However, limitations to the solutions to scientific problems still exist in this field …
Ensemble probabilistic wind power forecasting with multi-scale features
The uncertainty of wind power forecasting has a significant impact on the decision-making
process of power system operators. Herein, to comprehensively characterize this …
process of power system operators. Herein, to comprehensively characterize this …
Sparse variational Gaussian process based day-ahead probabilistic wind power forecasting
In this paper, we present a probabilistic wind power forecasting (PWPF) model via
quantification of epistemic uncertainty and aleatory uncertainty. Concretely, the epistemic …
quantification of epistemic uncertainty and aleatory uncertainty. Concretely, the epistemic …
Deep non-crossing probabilistic wind speed forecasting with multi-scale features
Clean and renewable wind energy has made an outstanding contribution to alleviating the
energy crisis. However, the randomness and volatility of wind brings great risk to the …
energy crisis. However, the randomness and volatility of wind brings great risk to the …
[HTML][HTML] Deep reinforcement learning based energy storage management strategy considering prediction intervals of wind power
Wind power generation combined with energy storage is able to maintain energy balance
and realize stable operation. This article proposes a data-driven energy storage …
and realize stable operation. This article proposes a data-driven energy storage …
Bidirectional gated recurrent unit-based lower upper bound estimation method for wind power interval prediction
F Liu, Q Tao, D Yang, D Sidorov - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
High-quality interval prediction is helpful to accurately capture the uncertainty of wind power
generation and provide support to grid dispatchers and operators. As an effective and …
generation and provide support to grid dispatchers and operators. As an effective and …
[HTML][HTML] Uncertainty quantification in sequential hybrid deep transfer learning for solar irradiation predictions
Hybrid deep learning model with multi-frequency capabilities is presented for simulating
solar irradiation. Utilizing hourly recorded solar irradiation and climate data, model employs …
solar irradiation. Utilizing hourly recorded solar irradiation and climate data, model employs …
Development and trending of deep learning methods for wind power predictions
H Liu, Z Zhang - Artificial Intelligence Review, 2024 - Springer
With the increasing data availability in wind power production processes due to advanced
sensing technologies, data-driven models have become prevalent in studying wind power …
sensing technologies, data-driven models have become prevalent in studying wind power …