Data mining and wind power prediction: A literature review

Wind power generated by wind turbines has a non-schedulable nature due to the stochastic nature of meteorological conditions. Hence, wind power predictions are required for few seconds to one week ahead in turbine control, load tracking, pre-load sharing, power system management and energy trading. In order to overcome problems in the predictions, many different wind power prediction models have been used to achieve in the literature. Data mining and its applications have more attention in recent years. This paper presents a review study banned on very short-term, short-term, medium-term and long-term wind power predictions. The studies available in the literature have been evaluated and criticized in consideration with their prediction accuracies and deficiencies. It is shown that adaptive neuro-fuzzy inference systems, neural networks and multilayer perceptrons give better results in wind power predictions.

[1]  YE Mehmet,et al.  User-Centered Interactive Data Mining : A Literature Review , 2010 .

[2]  Gaël Richard,et al.  Inferring Efficient Hierarchical Taxonomies for MIR Tasks: Application to Musical Instruments , 2005, ISMIR.

[3]  Chao Liu,et al.  Wind farm power prediction based on wavelet decomposition and chaotic time series , 2011, Expert Syst. Appl..

[4]  Luis Vargas,et al.  Data mining techniques for very short term prediction of wind power , 2010, 2010 IREP Symposium Bulk Power System Dynamics and Control - VIII (IREP).

[5]  A. Kusiak,et al.  Short-Term Prediction of Wind Farm Power: A Data Mining Approach , 2009, IEEE Transactions on Energy Conversion.

[6]  Amin Shokri Gazafroudi,et al.  A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada , 2013 .

[7]  Yongqian Liu,et al.  Genetic algorithm-piecewise support vector machine model for short term wind power prediction , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[8]  René Jursa,et al.  Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models , 2008 .

[9]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[10]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[11]  Zengyou He,et al.  Mining class outliers: concepts, algorithms and applications in CRM , 2004, Expert Syst. Appl..

[12]  T. Funabashi,et al.  Application of Recurrent Neural Network to Long-Term-Ahead Generating Power Forecasting for Wind Power Generator , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[13]  Shuhui Li,et al.  Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation , 2001 .

[14]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[15]  Pan Zhao,et al.  Neuro-fuzzy networks for short-term wind power forecasting , 2010, 2010 International Conference on Power System Technology.

[16]  Andrew Kusiak,et al.  On-line monitoring of power curves , 2009 .

[17]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[18]  Andrew Kusiak,et al.  Wind farm power prediction: a data‐mining approach , 2009 .

[19]  Andrew Kusiak,et al.  Models for monitoring wind farm power , 2009 .

[20]  M. Negnevitsky,et al.  Short term wind power forecasting using adaptive neuro-fuzzy inference systems , 2007, 2007 Australasian Universities Power Engineering Conference.

[21]  Nikos D. Hatziargyriou,et al.  Improved Wind Power Forecasting Using a Combined Neuro-fuzzy and Artificial Neural Network Model , 2006, SETN.

[22]  M. Y. Hussaini,et al.  Placement of wind turbines using genetic algorithms , 2005 .

[23]  Sun-Nien Yu,et al.  Actual experience on the short-term wind power forecasting at Penghu — From an island perspective , 2010, 2010 International Conference on Power System Technology.

[24]  Stéphanie Monjoly,et al.  An adaptive short-term prediction scheme for wind energy storage management , 2011 .

[25]  Yan Zhao,et al.  On Interactive Data Mining , 2008, Encyclopedia of Data Warehousing and Mining.

[26]  Silvia Zuffi,et al.  Human Computer Interaction: Legibility and Contrast , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[27]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[28]  Hong-shan Zhao,et al.  Optimization maintenance of wind turbines using Markov decision processes , 2010, 2010 International Conference on Power System Technology.

[29]  Zijun Zhang,et al.  Adaptive Control of a Wind Turbine With Data Mining and Swarm Intelligence , 2011, IEEE Transactions on Sustainable Energy.

[30]  Hui Liu,et al.  Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks , 2013 .

[31]  Xingpei Li,et al.  Short-term forecasting of wind turbine power generation based on Genetic Neural Network , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[32]  Ron Kohavi,et al.  Data Mining and Visualization , 2000 .

[33]  Zhancheng Guo,et al.  The intensification technologies to water electrolysis for hydrogen production - A review , 2014 .

[34]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[35]  Paras Mandal,et al.  Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[36]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[37]  Huei-Lin Chang,et al.  Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs , 2010 .

[38]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[39]  Andrew Kusiak,et al.  Power optimization of wind turbines with data mining and evolutionary computation , 2010 .

[40]  ChenYen-Liang,et al.  Mining fuzzy association rules from questionnaire data , 2009 .

[41]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[42]  P. Pinson,et al.  Probabilistic Forecasting of Wind Power at the Minute Time-Scale with Markov-Switching Autoregressive Models , 2008, Proceedings of the 10th International Conference on Probablistic Methods Applied to Power Systems.

[43]  Evangelos Triantaphyllou,et al.  Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications , 2008, Series on Computers and Operations Research.

[44]  Maria Grazia De Giorgi,et al.  Error analysis of short term wind power prediction models , 2011 .

[45]  Shane Phelan,et al.  Using Atmospheric Pressure Tendency to Optimise Battery Charging in Off-Grid Hybrid Wind-Diesel Systems for Telecoms , 2013 .

[46]  A. Testa,et al.  Very short-term probabilistic wind power forecasting based on Markov chain models , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[47]  H. M. I. Pousinho,et al.  An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[48]  Joao P. S. Catalao,et al.  Hybrid intelligent approach for short-term wind power forecasting in Portugal , 2011 .

[49]  Lei Dong,et al.  Wind power prediction using wavelet transform and chaotic characteristics , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.

[50]  Yiyu Yao,et al.  User-centered Interactive Data Mining , 2006, IEEE ICCI.

[51]  Ian Witten,et al.  Data Mining , 2000 .

[52]  Yen-Liang Chen,et al.  Mining fuzzy association rules from questionnaire data , 2009, Knowl. Based Syst..

[53]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[54]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[55]  Kay Chen Tan,et al.  A hybrid evolutionary algorithm for attribute selection in data mining , 2009, Expert Syst. Appl..

[56]  Chao Chen,et al.  A hybrid statistical method to predict wind speed and wind power , 2010 .

[57]  M. Negnevitsky,et al.  Data mining and analysis techniques in wind power system applications: abridged , 2006, 2006 IEEE Power Engineering Society General Meeting.

[58]  S. N. Sivanandam,et al.  Introduction to Data Mining and its Applications , 2006, Studies in Computational Intelligence.

[59]  Qi Luo,et al.  Advancing Knowledge Discovery and Data Mining , 2008, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008).

[60]  K. Agbossou,et al.  Nonlinear model identification of wind turbine with a neural network , 2004, IEEE Transactions on Energy Conversion.

[61]  M. Negnevitsky,et al.  Very short term wind power prediction: A data mining approach , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[62]  Joao P. S. Catalao,et al.  Short-term wind power forecasting in Portugal by neural networks and wavelet transform , 2011 .

[63]  Joao P. S. Catalao,et al.  A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal , 2011 .

[64]  Zijun Zhang,et al.  Short-Horizon Prediction of Wind Power: A Data-Driven Approach , 2010, IEEE Transactions on Energy Conversion.