A new approach to very short term wind speed prediction using k-nearest neighbor classification
Highlights
► Wind speed parameter was predicted in an n-tupled inputs using k-NN classification. ► The effects of input parameters, nearest neighbors and distance metrics were analyzed. ► Many useful and reasonable inferences were uncovered using the developed model.
Introduction
Wind energy has a critical role in the electricity generation based on renewable energy and its utilization ratio is increasing remarkably in the world. The most significant indicator of this case is that the global cumulative installed wind power capacity reached to 198 GW in 2010, while it was only 6.1 GW in 1996 [1], [2]. In wind energy systems, wind power generated by wind turbines has an intermittent and variable structure due to the stochastic nature of wind speed [3]. So, dispatchers need to predict the wind power for wind turbine control, pre-load sharing, power system management, energy trading, maintenance and repair of wind turbines [4]. As a result, wind speed is a crucial parameter for wind power integration.
Many methods have been developed in the field of wind speed prediction. These methods are classified into four categories as physical models, conventional statistical models, spatial correlation models and artificial intelligence models [5].
Physical models use physical data such as temperature, pressure, orography, roughness and obstacles in order to predict the wind speed, but good at long term wind speed prediction [5], [6]. Some of these models are Weather Research and Forecast (WRF) model [7], High Resolution Model (HRM) [8], Consortium for Small Scale Modeling (COSMO) [9], Mesoscale Model 5 (MM5) [10] and ETA model [11]. The spatial correlation methods consider the wind speed time series of the predicted sites and their neighboring sites in order to predict the wind speed, but these models have some challenges such as the need for measuring and transmitting the wind speeds of many correlated sites [5]. Fuzzy logic, neural network and fuzzy neural network models based on spatial correlation were implemented in [12], [13], [14].
Conventional statistical models use a mathematical model of the problem and give better results than physical models in very short term wind speed prediction [5], [6]. Some of these models are autoregressive (AR) models [15], autoregressive moving average (ARMA) models [16], [17], autoregressive integrated moving average (ARIMA) models [18], [19], Markov chain model [20] and Kalman filtering [21]. Artificial intelligence methods have a wide range of application in the field of wind speed prediction [5]. Radial basis functions (RBFs) [22], recurrent neural networks (RNNs) [23], multi-layer perceptrons (MLPs) [24], support vector machines (SVMs) [25], Bayesian learning model [26], fuzzy logic model [27], adaptive neuro-fuzzy inference model [28], [29] and k-means clustering model [30] were implemented in the literature.
The aim of this paper is, on the one hand, to predict the wind speed parameter using wind direction, air temperature, atmospheric pressure and relative humidity parameters in an n-tupled inputs with the k-NN classification model. On the other hand, the number of nearest neighbors, the dimension of input parameters and the selected distance metric were analyzed for investigating their effects on the prediction performance. Many useful and reasonable inferences were uncovered for wind speed prediction in this paper.
The rest of the paper is organized as follows. The developed k-NN classification model and the selected distance metrics are introduced in Section 2. Section 3 describes the dataset used for this study and explains the data cleaning process. The wind speed prediction results based on 3-tupled and 4-tupled inputs are presented and discussed in Section 4. Section 5 concludes the work.
Section snippets
k-Nearest neighbor classification
Data mining is a process of knowledge extraction used for revealing previously unknown, hidden, meaningful and useful patterns in databases [31]. k-NN classification has a wide range of application in the field of data mining and based on using specific training instances to make a class prediction for a new unclassified instance [32]. Euclidean distance metric is often used for measuring the closeness of the data points in k-NN classification [33]. Differently, Manhattan and Minkowski distance
Data description and data preprocessing
The dataset used in this study was generated by a meteorological station placed in Poyracık, Turkey. Poyracık has an elevation of 125 m and its latitude and longitude are 39°9′N and 27°29′E, respectively. Its annual average temperature, relative humidity and rainfall are 17.9 °C, 54.52% and 57.05 kg/m2, respectively [37]. In addition, its wind climate regime depends on Etesian winds in summer as a result of the high pressure over the Balkans and the heating of the Anatolian plateau [38]. The total
Multi-tupled inputs in wind speed prediction
A small error in wind speed prediction leads to a large error in wind power prediction [42]. Therefore, it is needed to predict the wind speed parameter in an accurate way. For this purpose, a k-NN classification model was developed and used for predicting the wind speed parameter in this study. Unlike the studies in the literature, the model developed predicts the wind speed parameter in an n-tupled inputs. In here, each dimension represents each input parameter as described in [43], [44]. For
Conclusions
This paper presented a new approach based on k-NN classification to short term wind speed prediction using multi-tupled inputs. The k-NN classification model developed included not only Euclidean, Manhattan and Minkowski distance metrics but also MAE, MAPE and NRMSE metrics. The numerical classification results were achieved rapidly by means of the object oriented programming techniques adapted to the model developed. Besides, the observed and the predicted wind speeds along with their error
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