A modified Elman neural network with a new learning rate scheme
Elman neural network (ENN) is one of recurrent neural networks (RNNs). Comparing to
traditional neural networks, ENN has additional inputs from the hidden layer, which forms a …
traditional neural networks, ENN has additional inputs from the hidden layer, which forms a …
A Data Mining Approach Combining -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting
Wind power forecasting (WPF) is significant to guide the dispatching of grid and the
production planning of wind farm effectively. The intermittency and volatility of wind leading …
production planning of wind farm effectively. The intermittency and volatility of wind leading …
Probabilistic forecast of PV power generation based on higher order Markov chain
This paper presents a method to forecast the probability distribution function (PDF) of the
generated power of PV systems based on the higher order Markov chain (HMC). Since the …
generated power of PV systems based on the higher order Markov chain (HMC). Since the …
Short-term power load forecasting based on Elman neural network with particle swarm optimization
K **e, H Yi, G Hu, L Li, Z Fan - Neurocomputing, 2020 - Elsevier
The prediction of short term power load owns a great influence on performance of the whole
electric system. Raising the result of power load forecasting is always a research spot. This …
electric system. Raising the result of power load forecasting is always a research spot. This …
Self-adaptive discrete grey model based on a novel fractional order reverse accumulation sequence and its application in forecasting clean energy power generation …
With the increasing power consumption in China and the urgent demand for environmental
protection, promoting the development of clean energy power generation industry is the only …
protection, promoting the development of clean energy power generation industry is the only …
Short-term wind power forecasting on multiple scales using VMD decomposition, K-means clustering and LSTM principal computing
Z Sun, S Zhao, J Zhang - IEEE access, 2019 - ieeexplore.ieee.org
Wind power plays a crucial role in the secure conversion and management of the power
system. Therefore, this study proposes a hybrid model for short-term wind power forecasting …
system. Therefore, this study proposes a hybrid model for short-term wind power forecasting …
Interval prediction of solar power using an improved bootstrap method
The integration of solar energies into power grid requires accurate prediction of solar power.
While most previous literature is focused on how to improve the accuracy of point forecast …
While most previous literature is focused on how to improve the accuracy of point forecast …
A contrastive learning-based framework for wind power forecast
The feature representation of wind power sequences is crucial in the modeling of short-tern
wind power forecast, but the existing feature representation methods mostly depend on the …
wind power forecast, but the existing feature representation methods mostly depend on the …
Ultra-short-term wind power combined prediction based on complementary ensemble empirical mode decomposition, whale optimisation algorithm, and elman …
A Zhu, Q Zhao, X Wang, L Zhou - Energies, 2022 - mdpi.com
Accurate wind power forecasting helps relieve the regulation pressure of a power system,
which is of great significance to the power system's operation. However, achieving …
which is of great significance to the power system's operation. However, achieving …
Short‐term wind power forecasting based on two‐stage attention mechanism
Wind power is usually closely related to the meteorological information around the wind
farm, which leads to the fluctuation of wind power and makes it difficult to predict precisely. In …
farm, which leads to the fluctuation of wind power and makes it difficult to predict precisely. In …