Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)
出版年份 2014 全文链接
标题
Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)
作者
关键词
-
出版物
Energies
Volume 7, Issue 8, Pages 5251-5272
出版商
MDPI AG
发表日期
2014-08-14
DOI
10.3390/en7085251
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks
- (2013) Luis Hernández et al. Energies
- Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting
- (2013) José Palomares-Salas et al. Energies
- Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition
- (2013) Ying-Yi Hong et al. Energies
- Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
- (2013) Wen-Yeau Chang Energies
- Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting
- (2013) Guo-Feng Fan et al. Energies
- Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model
- (2013) Jie Wu et al. ENERGY CONVERSION AND MANAGEMENT
- Performance measurements of monocrystalline silicon PV modules in South-eastern Italy
- (2013) P.M. Congedo et al. ENERGY CONVERSION AND MANAGEMENT
- Hybrid Kalman Filters for Very Short-Term Load Forecasting and Prediction Interval Estimation
- (2013) Che Guan et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- A hybrid forecasting approach applied to wind speed time series
- (2013) Jianming Hu et al. RENEWABLE ENERGY
- Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
- (2013) Hao Quan et al. IEEE Transactions on Neural Networks and Learning Systems
- A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power
- (2012) Qian Zhang et al. Energies
- Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm
- (2012) Hongze Li et al. Energies
- Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks
- (2012) George Sideratos et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Predicting the energy output of wind farms based on weather data: Important variables and their correlation
- (2012) Ekaterina Vladislavleva et al. RENEWABLE ENERGY
- Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods
- (2011) Maria Grazia De Giorgi et al. ENERGY
- Fine tuning support vector machines for short-term wind speed forecasting
- (2011) Junyi Zhou et al. ENERGY CONVERSION AND MANAGEMENT
- Wind farm power prediction based on wavelet decomposition and chaotic time series
- (2011) Xueli An et al. EXPERT SYSTEMS WITH APPLICATIONS
- Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
- (2011) A. Khosravi et al. IEEE TRANSACTIONS ON NEURAL NETWORKS
- Current methods and advances in forecasting of wind power generation
- (2011) Aoife M. Foley et al. RENEWABLE ENERGY
- On comparing three artificial neural networks for wind speed forecasting
- (2010) Gong Li et al. APPLIED ENERGY
- Error analysis of short term wind power prediction models
- (2010) Maria Grazia De Giorgi et al. APPLIED ENERGY
- Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece
- (2009) G. Xydis et al. APPLIED ENERGY
- Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor
- (2009) M. Beccali et al. APPLIED ENERGY
- Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm
- (2008) N AMJADY et al. ENERGY
- Statistical Analysis of Wind Power Forecast Error
- (2008) H. Bludszuweit et al. IEEE TRANSACTIONS ON POWER SYSTEMS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started