Development of methods for regional wind power forecasting
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[1] Shuhui Li,et al. Using neural networks to estimate wind turbine power generation , 2001 .
[2] G. Galanisa,et al. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering , 2006 .
[3] I. Miller. Probability, Random Variables, and Stochastic Processes , 1966 .
[4] Henrik Madsen,et al. A new reference for wind power forecasting , 1998 .
[5] Ismael Sánchez,et al. Short-term prediction of wind energy production , 2006 .
[6] Georges Kariniotakis. Contribution au développement d'un système de controle avancé pour les systèmes éolien-diesel autonomes , 1996 .
[7] G. Kariniotakis,et al. A Stochastic Dynamic Programming Model for Optimal Use of Local Energy Resources in a Market Environment , 2007, 2007 IEEE Lausanne Power Tech.
[8] Yozo Fujino,et al. A physical-statistical approach for the regional wind power forecasting , 2007 .
[9] Geoff Holmes,et al. Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..
[10] Pierre Pinson,et al. Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models , 2005 .
[11] J.A.P. Lopes,et al. On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.
[12] Atam P. Dhawan,et al. Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring , 1993, IEEE International Conference on Neural Networks.
[13] Sven-Erik Thor,et al. Long‐term research and development needs for wind energy for the time frame 2000–2020 , 2002 .
[14] Georges Kariniotakis,et al. Short-term Wind Power Forecasting Using Advanced Statistical Methods , 2006 .
[15] Pierre Pinson,et al. On‐line assessment of prediction risk for wind power production forecasts , 2003 .
[16] Spyros Makridakis,et al. The M3-Competition: results, conclusions and implications , 2000 .
[17] Yozo Fujino,et al. An upscaling approach for the regional wind power forecasting , 2007 .
[18] George Stavrakakis,et al. Wind power forecasting using advanced neural networks models , 1996 .
[19] V. Vapnik. Estimation of Dependences Based on Empirical Data , 2006 .
[20] David A. Bell,et al. A Formalism for Relevance and Its Application in Feature Subset Selection , 2000, Machine Learning.
[21] Detlev Heinemann,et al. Enhanced regional forecasting considering single wind farm distribution for upscaling , 2007 .
[22] Georges Kariniotakis,et al. An advanced On-line Wind Resource Prediction system for the optimal management of wind park , 2002 .
[23] Georges Kariniotakis,et al. Optimizing Benefits from wind power participation in electricity market using advanced tools for wind power forecasting and uncertainty assessment , 2004 .
[24] A. S. Weigend,et al. Selecting Input Variables Using Mutual Information and Nonparemetric Density Estimation , 1994 .
[25] Sami Repo,et al. Prediction errors and balancing costs for wind power production in Finland , 2006 .
[26] Li-Xin Wang,et al. Adaptive fuzzy systems and control - design and stability analysis , 1994 .
[27] Vladimiro Miranda,et al. Training a FIS with EPSO under an Entropy Criterion for Wind Power prediction , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.
[28] D. Paravan,et al. Selecting input variables for HPP reservoir water inflow forecasting using mutual information , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).
[29] C. A. Murthy,et al. Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[30] Pat Langley,et al. An Analysis of Bayesian Classifiers , 1992, AAAI.
[31] Yann LeCun,et al. Une procedure d'apprentissage pour reseau a seuil asymmetrique (A learning scheme for asymmetric threshold networks) , 1985 .
[32] Frank Rosenblatt,et al. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .
[33] Henrik Madsen,et al. Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts , 2006 .
[34] P. Pinson,et al. What performance can be expected by short-term wind power prediction models depending on site characteristics? , 2004 .
[35] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[36] M. J. Grubb,et al. Value of variable sources on power systems , 1991 .
[37] Henrik Madsen,et al. Using meteorological forecasts in on-line predictions of wind power. , 1997 .
[38] Veda C. Storey,et al. A Framework for Analysis of Data Quality Research , 1995, IEEE Trans. Knowl. Data Eng..
[39] Poul Alberg Østergaard,et al. Ancillary services and the integration of substantial quantities of wind power , 2006 .
[40] Torben Skov Nielsen,et al. Online prediction and control in nonlinear stochastic systems , 2002 .
[41] Pierre Pinson,et al. The state of the art in short term prediction of wind power - from an offshore perspective , 2004 .
[42] John Bjørnar Bremnes,et al. Probabilistic wind power forecasts using local quantile regression , 2004 .
[43] Tommy W. S. Chow,et al. Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information , 2005, IEEE Transactions on Neural Networks.
[44] Eamon McKeogh,et al. Aggregate Forecasting of Wind Generation on the Irish Grid Using a Multi-Scheme Ensemble Prediction System , 2006 .
[45] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[46] Philip Hans Franses,et al. The M3 competition: Statistical tests of the results , 2005 .
[47] Georges Kariniotakis,et al. Evaluation of advanced wind power and load forecasting methods for the optimal management of isolated power systems , 1999 .
[48] Ronald L. Rivest,et al. Training a 3-node neural network is NP-complete , 1988, COLT '88.
[49] D. Seborg,et al. Clustering multivariate time‐series data , 2005 .
[50] P. Pinson,et al. Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power , 2007, IEEE Transactions on Power Systems.
[51] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[52] Kari Torkkola,et al. Feature Extraction by Non-Parametric Mutual Information Maximization , 2003, J. Mach. Learn. Res..
[53] R. Castro,et al. A Comparison Between Chronological and Probabilistic Methods to Estimate Wind Power Capacity Credit , 2001, IEEE Power Engineering Review.
[54] Robert Babuska,et al. Input selection for nonlinear regression models , 2004, IEEE Transactions on Fuzzy Systems.
[55] Andreas S. Weigend,et al. Nonparametric selection of input variables for connectionist learning , 1996 .
[56] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[57] M. Lange. On the Uncertainty of Wind Power Predictions—Analysis of the Forecast Accuracy and Statistical Distribution of Errors , 2005 .
[58] M. Milligan,et al. Estimating the Economic Value of Wind Forecasting to Utilities , 1995 .
[59] S. Enomoto. PREDICTION OF POWER OUTPUT FROM WIND FARM USING LOCAL METEOROLOGICAL ANALYSIS , 2001 .
[60] C. Siriopoulos,et al. Time series forecasting of averaged data with efficient use of information , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.
[61] Craig B. Borkowf,et al. Time-Series Forecasting , 2002, Technometrics.
[62] Richard L. Ottinger,et al. Compendium of Sustainable Energy Laws: Directive 2001/77/EC of the European Parliament and of the Council of 27 September 2001 on the Promotion of Electricity Produced from Renewable Energy Sources in the Internal Electricity Market , 2005 .
[63] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[64] Siegfried Heier,et al. Grid Integration of Wind Energy Conversion Systems , 1998 .
[65] R. Wiser,et al. Policies and market factors driving wind power development in the United States , 2005 .
[66] M. Lange,et al. Previento-A Wind Power Prediction System with an Innovative Upscaling Algorithm , 2001 .
[67] Roberto Battiti,et al. Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.
[68] Georges Kariniotakis,et al. Load and wind power forecasting methods for the optimal management of isolated power systems with high wind penetration , 1999 .
[69] Lise Getoor,et al. Learning in Logic , 2010, Encyclopedia of Machine Learning.
[70] Y. Wan,et al. Statistical Wind Power Forecasting for U.S. Wind Farms: Preprint , 2003 .
[71] Vladimir Naumovich Vapni. The Nature of Statistical Learning Theory , 1995 .
[72] P. S. Dokopoulos,et al. Wind speed and power forecasting based on spatial correlation models , 1999 .
[73] Richard Bellman,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[74] Gregor Giebel,et al. Wind Power Prediction using Ensembles , 2005 .
[75] Dennis Anderson,et al. Harvesting and redistributing renewable energy: on the role of gas and electricity grids to overcome intermittency through the generation and storage of hydrogen , 2004 .
[76] Georges Kariniotakis,et al. Quantification of capacity credit and reserve requirements from the large scale integration of wind energy in the french power system , 2004 .
[77] Nir Friedman,et al. Bayesian Network Classifiers , 1997, Machine Learning.
[78] Paul Denholm,et al. Improving the technical, environmental and social performance of wind energy systems using biomass-based energy storage , 2006 .
[79] P. Smith,et al. Grid code provisions for wind generators in Ireland , 2005, IEEE Power Engineering Society General Meeting, 2005.
[80] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[81] G.N. Kariniotakis,et al. Probabilistic Short-term Wind Power Forecasting for the Optimal Management of Wind Generation , 2007, 2007 IEEE Lausanne Power Tech.
[82] Henrik Madsen,et al. The Zephyr project - The next generation prediction system (poster) , 2001 .
[83] Henrik Madsen,et al. Wind power Ensemble forecasting , 2004 .
[84] Georges Kariniotakis,et al. On-line adaptation of confidence intervals based on weather stability for wind power forecasting , 2004 .
[85] Ulrich Focken,et al. Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects , 2002 .
[86] R. Buizza. Potential Forecast Skill of Ensemble Prediction and Spread and Skill Distributions of the ECMWF Ensemble Prediction System , 1997 .
[87] Eamon McKeogh,et al. Application of a Multi-Scheme Ensemble Prediction System for wind power forecasting in Ireland and comparison with validation results from Denmark and Germany , 2005 .
[88] E.A. DeMeo,et al. Wind plant integration [wind power plants] , 2005, IEEE Power and Energy Magazine.
[89] J. P. Marques de Sá,et al. Pattern Recognition: Concepts, Methods and Applications , 2001 .
[90] H. Madsen,et al. PREDICTION OF REGIONAL WIND POWER , 2022 .
[91] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[92] Lars Landberg,et al. Short-term prediction of local wind conditions , 1994 .
[93] Steven C. Wheelwright,et al. Forecasting: Methods and Applications, 3rd Ed , 1997 .
[94] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[95] P. B. Eriksen,et al. System operation with high wind penetration , 2005, IEEE Power and Energy Magazine.
[96] Wim Turkenburg,et al. Assessment of the global and regional geographical, technical and economic potential of onshore wind energy , 2004 .
[97] Richard Y. Wang,et al. Anchoring data quality dimensions in ontological foundations , 1996, CACM.
[98] R. Gray. Entropy and Information Theory , 1990, Springer New York.
[99] Ferdinand Baer,et al. Numerical weather prediction , 2000, Adv. Comput..
[100] Athanasios Sfetsos,et al. A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .
[101] A. Fabbri,et al. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.
[102] Georges Kariniotakis,et al. Forecasting of regional wind generation by a dynamic fuzzy-neural networks based upscaling approach , 2003 .
[103] Pierre Pinson,et al. Estimation of the uncertainty in wind power forecasting , 2006 .
[104] Kang Li. A new input selection method for neural modeling of nonlinear complex systems , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).
[105] H. Beyer,et al. REDUCTION OF WINDPOWER PREDICTION ERROR BY SPATIAL SMOOTHING EFFECTS , 2001 .
[106] Bernhard Ernst,et al. ONLINE-MONITORING AND PREDICTION OF WIND POWER IN GERMAN TRANSMISSION SYSTEM OPERATION CENTRES , 2002 .
[107] T. Jenkin,et al. Opportunities for Electricity Storage in Deregulating Markets , 1999 .