A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction

Abstract With the growing share of wind power production in the electric power grids, many critical challenges to the grid operators have been emerged in terms of the power balance, power quality, voltage support, frequency stability, load scheduling, unit commitment and spinning reserve calculations. To overcome such problems, numerous studies have been conducted to predict the wind power production, but a small number of them have attempted to improve the prediction accuracy by employing the multidimensional meteorological input data. The novelties of this study lie in the proposal of an efficient and easy to implement very short-term wind power prediction model based on the k-nearest neighbor classifier (kNN), in the usage of wind speed, wind direction, barometric pressure and air temperature parameters as the multi-tupled meteorological inputs and in the comparison of wind power prediction results with respect to the persistence reference model. As a result of the achieved patterns, we characterize the variation of wind power prediction errors according to the input tuples, distance measures and neighbor numbers, and uncover the most influential and the most ineffective meteorological parameters on the optimization of wind power prediction results.

[1]  Zara Ghodsi,et al.  From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory , 2016, Digit. Signal Process..

[2]  Seddik Bacha,et al.  Modeling and control of hybrid photovoltaic wind power system with battery storage , 2015 .

[3]  Hamidreza Zareipour,et al.  Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm , 2015 .

[4]  Seref Sagiroglu,et al.  A new approach to very short term wind speed prediction using k-nearest neighbor classification , 2013 .

[5]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[6]  Yuehua Huang,et al.  Short-term wind power prediction based on LSSVM–GSA model , 2015 .

[7]  George Galanis,et al.  Wind power prediction based on numerical and statistical models , 2013 .

[8]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[9]  Ali Reza Seifi,et al.  Expert energy management of a micro-grid considering wind energy uncertainty , 2014 .

[10]  Seref Sagiroglu,et al.  A novel intelligent approach for yaw position forecasting in wind energy systems , 2015 .

[11]  D. Larose.  k‐Nearest Neighbor Algorithm , 2005 .

[12]  Alfred Baghramian,et al.  A novel heuristic method for wind farm power prediction: A case study , 2014 .

[13]  Jarmo Partanen,et al.  Technical, economic and uncertainty modelling of a wind farm project , 2016 .

[14]  Humberto Verdejo,et al.  Stochastic modeling to represent wind power generation and demand in electric power system based on real data , 2016 .

[15]  Feng Gao,et al.  Sparse online warped Gaussian process for wind power probabilistic forecasting , 2013 .

[16]  Joao P. S. Catalao,et al.  Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information , 2015 .

[17]  A. Testa,et al.  Markov chain modeling for very-short-term wind power forecasting , 2015 .

[18]  Jun Liang,et al.  Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis , 2015 .

[19]  Asifullah Khan,et al.  Machine Learning based short term wind power prediction using a hybrid learning model , 2015, Comput. Electr. Eng..

[20]  Akin Tascikaraoglu,et al.  The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems , 2012 .

[21]  Mansi Ghodsi,et al.  Do trend extraction approaches affect causality detection in climate change studies? , 2017 .

[22]  Umberto Desideri,et al.  Transition of clean energy systems and technologies towards a sustainable future (Part I) , 2015 .

[23]  Yachao Zhang,et al.  Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods , 2016 .

[24]  Shahaboddin Shamshirband,et al.  Design and state of art of innovative wind turbine systems , 2016 .

[25]  S. Iranmanesh,et al.  A COMPARISON OF TWO APPROACHES FOR INTEGRAL PURCHASING MANAGEMENT IN AN OIL-AND-GAS COMPANY (PETROPARS) , 2013 .

[26]  Stefano Alessandrini,et al.  A novel application of an analog ensemble for short-term wind power forecasting , 2015 .

[27]  Rob Sullivan.  Classification and Prediction , 2012 .

[28]  Farid Melgani,et al.  Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Yiping Dai,et al.  Capacity allocation of a hybrid energy storage system for power system peak shaving at high wind power penetration level , 2015 .

[30]  I. González-Aparicio,et al.  Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain , 2015 .

[31]  K. Afshar,et al.  Reliability and economic evaluation of demand side management programming in wind integrated power systems , 2016 .

[32]  Seref Sagiroglu,et al.  Data mining and wind power prediction: A literature review , 2012 .

[33]  Nasrudin Abd Rahim,et al.  Using data-driven approach for wind power prediction: A comparative study , 2016 .

[34]  Antonio Messineo,et al.  Monitoring of wind farms’ power curves using machine learning techniques , 2012 .

[35]  Jiang Wu,et al.  Aggregated wind power generation probabilistic forecasting based on particle filter , 2015 .

[36]  Ekaterina Mangalova,et al.  K-nearest neighbors for GEFCom2014 probabilistic wind power forecasting , 2016 .

[37]  Arash Miranian,et al.  A DEVELOPED WAVELET-BASED LOCAL LINEAR NEURO FUZZY MODEL FOR THE FORECASTING OF CRUDE OIL PRICE , 2013 .

[38]  Jianzhou Wang,et al.  A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China , 2014 .

[39]  Matti Lehtonen,et al.  Statistical Modeling of Aggregated Electricity Consumption and Distributed Wind Generation in Distribution Systems Using AMR Data , 2015 .