Application of support vector machine models for forecasting solar and wind energy resources: A review
Conventional fossil fuels are depleting daily due to the growing human population. Previous
research has proved that renewable energy sources, especially solar and wind, can be …
research has proved that renewable energy sources, especially solar and wind, can be …
[HTML][HTML] Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain
Wind power forecasting has supported operational decision-making for power system and
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
electricity markets for 30 years. Efforts of improving the accuracy and/or certainty of …
Short-term wind power forecasting approach based on Seq2Seq model using NWP data
Y Zhang, Y Li, G Zhang - Energy, 2020 - Elsevier
Wind power is one of the main sources of renewable energy. Precise forecast of the power
output of wind farms could greatly decrease the negative impact of wind power on power …
output of wind farms could greatly decrease the negative impact of wind power on power …
Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features
Very-short term wind power forecasting is one of the most effective ways to deal with the
challenges of increased penetration of wind power into the electric grid due to its fluctuation …
challenges of increased penetration of wind power into the electric grid due to its fluctuation …
Short-term wind-power prediction based on wavelet transform–support vector machine and statistic-characteristics analysis
The prediction algorithm is one of the most important factors in the quality of wind-power
prediction. In this paper, based on the principles of wavelet transform and support vector …
prediction. In this paper, based on the principles of wavelet transform and support vector …
A novel hybrid approach based on relief algorithm and fuzzy reinforcement learning approach for predicting wind speed
Wind speed (WS) prediction has become popular nowadays due to increasing demand for
wind power generation and competitive development in wind energy. Many prediction …
wind power generation and competitive development in wind energy. Many prediction …
基于卷积循环神经网络的风电场内多点位风速预测方法
梁超， 刘永前， 周家慷， 阎洁， 鲁宗相 - 电网技术, 2020 - epjournal.csee.org.cn
传统的超短期风速预测方法往往采用风电场内单一位置处风速信号进行预测,
忽略了风电机组间的风速相关性, 导致预测模型难以考虑地形和尾流影响下的风速空间分布特征 …
忽略了风电机组间的风速相关性, 导致预测模型难以考虑地形和尾流影响下的风速空间分布特征 …
A deep learning framework for day ahead wind power short-term prediction
P Xu, M Zhang, Z Chen, B Wang, C Cheng, R Liu - Applied sciences, 2023 - mdpi.com
Due to the increasing proportion of wind power connected to the grid, day-ahead wind
power prediction plays a more and more important role in the operation of the power system …
power prediction plays a more and more important role in the operation of the power system …
Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine
J Yan, Y Liu, S Han, M Qiu - Renewable and sustainable energy reviews, 2013 - Elsevier
Relevance vector machine, a sparse probabilistic learning machine based on the kernel
function, has excellent ability of prediction and generalization. It is proposed by this paper …
function, has excellent ability of prediction and generalization. It is proposed by this paper …
Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning machines
M Poursaeid, R Mastouri, S Shabanlou… - Environmental Earth …, 2020 - Springer
In this study, the groundwater parameters including electrical conductivity (EC), salinity, total
dissolved solids (TDS) and groundwater level (GWL) for a 15-year time series (from 2002 to …
dissolved solids (TDS) and groundwater level (GWL) for a 15-year time series (from 2002 to …