A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
出版年份 2021 全文链接
标题
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
作者
关键词
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出版物
Energies
Volume 14, Issue 16, Pages 5196
出版商
MDPI AG
发表日期
2021-08-23
DOI
10.3390/en14165196
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM
- (2021) Qianyu Wu et al. IET Renewable Power Generation
- Comparative Assessment of Regression Techniques for Wind Power Forecasting
- (2021) Rachna Pathak et al. IETE JOURNAL OF RESEARCH
- Chaos cloud quantum bat hybrid optimization algorithm
- (2021) Ming-Wei Li et al. NONLINEAR DYNAMICS
- Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm
- (2021) Guangyu Qin et al. Sustainability
- Enhancing Wind Turbine Power Forecast via Convolutional Neural Network
- (2021) Tianyang Liu et al. Electronics
- A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting
- (2021) Yingchao Dong et al. APPLIED ENERGY
- Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting
- (2021) Fei Zhang et al. RENEWABLE ENERGY
- Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology
- (2021) Changfei Zhao et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction
- (2020) Branko Kosovic et al. Energies
- A Critical Review of Wind Power Forecasting Methods—Past, Present and Future
- (2020) Shahram Hanifi et al. Energies
- Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network
- (2020) Zi Lin et al. ENERGY
- A Survey of Machine Learning Models in Renewable Energy Predictions
- (2020) Jung-Pin Lai et al. Applied Sciences-Basel
- A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting
- (2020) Hamed H.H. Aly ENERGY
- Wind power forecasting – A data-driven method along with gated recurrent neural network
- (2020) Adam Kisvari et al. RENEWABLE ENERGY
- Day-ahead wind power forecasting based on the clustering of equivalent power curves
- (2020) Mao Yang et al. ENERGY
- Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation
- (2020) Bo Gu et al. RENEWABLE ENERGY
- Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches
- (2019) Saman Maroufpoor et al. INTERNATIONAL JOURNAL OF CLIMATOLOGY
- Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system
- (2019) Han Wang et al. APPLIED ENERGY
- Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model
- (2019) Jinhua Zhang et al. APPLIED ENERGY
- Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network
- (2019) Cong Wang et al. APPLIED ENERGY
- Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
- (2019) Ling-Ling Li et al. JOURNAL OF CLEANER PRODUCTION
- A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning
- (2019) Gang Wang et al. RENEWABLE ENERGY
- Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting
- (2019) Ciaran Gilbert et al. IEEE Transactions on Sustainable Energy
- Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression
- (2018) Muhammad Waseem Ahmad et al. ENERGY
- Deep belief network based k-means cluster approach for short-term wind power forecasting
- (2018) Kejun Wang et al. ENERGY
- Advanced wind power prediction based on data-driven error correction
- (2018) Jing Yan et al. ENERGY CONVERSION AND MANAGEMENT
- Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal
- (2018) Yong Qin et al. APPLIED ENERGY
- LSTM-EFG for wind power forecasting based on sequential correlation features
- (2018) Ruiguo Yu et al. Future Generation Computer Systems-The International Journal of eScience
- Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine
- (2017) Tian Peng et al. ENERGY CONVERSION AND MANAGEMENT
- Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting
- (2017) Fei Wang et al. Applied Sciences-Basel
- Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm
- (2016) Yan Chen et al. Energies
- A hybrid wind power forecasting model based on data mining and wavelets analysis
- (2016) R. Azimi et al. ENERGY CONVERSION AND MANAGEMENT
- Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression
- (2016) Qinghua Hu et al. IEEE Transactions on Sustainable Energy
- Machine Learning based short term wind power prediction using a hybrid learning model
- (2015) Najeebullah et al. COMPUTERS & ELECTRICAL ENGINEERING
- Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
- (2015) Song Li et al. IEEE Transactions on Sustainable Energy
- Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm
- (2015) Yu JIANG et al. Journal of Modern Power Systems and Clean Energy
- Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting
- (2013) Guo-Feng Fan et al. Energies
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