Mehmet Yesilbudak

Prediction of solar radiation using meteorological data

Solar radiation prediction has a great importance in electricity generation from solar energy and helps to size photovoltaic power systems. Therefore, the solar radiation parameter was predicted at 10-min intervals in this study. Outside temperature, outside humidity and barometric pressure parameters were used as meteorological input variables by the developed k-nearest neighbor (k-NN) classifier. On the one hand, it is mined that solar radiation prediction was affected by the number of nearest neighbors, the dimension of input parameters and the type of distance metrics. On the other hand, it is shown that the k-NN classifier which uses Euclidean distance metric for k=4 in 3-dimensional input space outperformed the other models in terms of the prediction accuracy. Adversely, the k-NN classifier which only uses barometric pressure input provided the weakest prediction performance for k=15 in Euclidean distance metric.

A review of data mining and solar power prediction

Solar energy is one of the clean and renewable energy sources that are mostly available in the world. As a result of this situation, there are many research studies done on the solar energy in order to get the maximum solar radiation during the day time, to estimate the solar power generation and to increase the efficiency of solar systems. In this paper, especially, a review of data mining methods employed for solar power prediction in the literature is introduced briefly. Input data, recording intervals, the number of training and test datasets of each study are also considered in the review process. It is shown that artificial neural networks are the most preferred methods in order to predict solar power generation.

Design of an Intelligent Decision Making System for a Travelling Wave Ultrasonic Motor

In this study, an intelligent decision making system which determines the compatibility of operating parameters has been designed for the travelling wave ultrasonic motor (TWUSM). The system designed converts input parameters and operating temperature into useful data by rule-based inference mechanism and these data are evaluated in Naïve Bayes Classifier. The proposed decision making system gives effective results in the compatibility determination of operating parameters for speed stability of the TWUSM.

Multi-period Prediction of Solar Radiation Using ARMA and ARIMA Models

Due to the variations in weather conditions, solar power integration to the electricity grid at a high penetration rate can cause a threat for the grid stability. Therefore, it is required to predict the solar radiation parameter in order to ensure the quality and the security of the grid. In this study, initially, a 1-h time series model belong to the solar radiation parameter is created for multi-period predictions. Afterwards, autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are compared in terms of the goodness-of-fit value produced by the log-likelihood function. As a result of determining the best statistical models in multi-period predictions, one-period, two-period and three-period ahead predictions are carried out for the solar radiation parameter in a comprehensive way. Many feasible comparisons have been made for the solar radiation prediction.

Multi-time series and -time scale modeling for wind speed and wind power forecasting part I: Statistical methods, very short-term and short-term applications

This study concentrates on multi-time series and - time scale modeling in wind speed and wind power forecasting. Different statistical models along with different time horizons are analyzed and evaluated broadly and comprehensively. For this reason, the entire study is divided into two main scientific parts. In case of making a general overview of the entire study, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) methods are employed for multi-time series modeling. Very short-term, short-term, medium-term and long-term scales are utilized for multi-time scale modeling. Specifically, in this part of the entire study, the mentioned statistical models are presented in detail and 10-min and 1-h time series forecasting models are created for the purpose of achieving 10-min and 2-h ahead forecasting, respectively. Many useful outcomes are accomplished for very short-term and short-term wind speed and wind power forecasting.

Multi-time series and -time scale modeling for wind speed and wind power forecasting part II: Medium-term and long-term applications

This paper represents the second part of an entire study which focuses on multi-time series and -time scale modeling in wind speed and wind power forecasting. In the first part of the entire study [1], firstly, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are introduced in-depth. Afterwards, the mentioned models are analyzed for very short-term and short-term forecasting scales, comprehensively. In this second part of the entire study, we address the medium-term and long-term prediction performance of MA, WMA, ARMA and ARIMA models in wind speed and wind power forecasting. Particularly, 3-h and 6-h time series forecasting models are constructed in order to carry out 9-h and 24-h ahead forecasting, respectively. Many valuable assessments are made for the employed statistical models in terms of medium-term and long-terms forecasting scales. Finally, many valuable achievements are discussed considering a detailed comparison chart of the entire study.

An intelligent approach for speed stability analysis of a travelling wave ultrasonic motor based on genetic k-NN algorithm

Driving voltage, driving frequency, phase difference and operating temperature are the parameters which affect the speed stability of a travelling wave ultrasonic motor (TWUSM). The weight coefficients of these parameters should be determined for the purpose of ensuring the speed stability of a TWUSM with a maximum level under different operating conditions. In this paper, a novel approach is proposed for the speed stability analysis of the TWUSM using genetic k-nearest neighbor algorithm (k-NN) and the speed stability classes of new test observations are achieved accurately. Furthermore, the genetic k-NN algorithm is compared with the classic k-NN algorithm in terms of prediction accuracy using Euclidean, Manhattan and Minkowski distance metrics. As a result of experimental studies, it is shown that the TWUSM parameters weighted by the genetic k-NN algorithm increase the speed stability of the TWUSM significantly and the genetic k-NN algorithm outperforms the classic k-NN algorithm for all of distance metrics.

Clustering analysis of multidimensional wind speed data using k-means approach

The energy capability of wind power plants is strictly correlated with the wind characteristics of the considered site. For this reason, it is very important to process the wind speed data for making the wind power more competitive with respect to other energy sources. This paper presents a detailed similarity analysis to discover the meaningful subsets within the monthly average wind speed data of 75 provinces in Turkey. In the similarity analysis, the k-means clustering method is adapted with Squared Euclidean, City-Block, Cosine and Pearson Correlation distance measures. In addition, the silhouette coefficient is used to validate how well-separated the resulting clusters are. As a result of the optimal silhouettes acquired for k=5 and Squared Euclidean distance measure, many comparative assessments are made about the monthly average wind speed characteristics of all provinces.

Design and simulation of a drive system for speed control of travelling wave ultrasonic motor

This paper presents a low cost, user friendly and effective drive system for the speed control of travelling wave ultrasonic motor (TWUSM). Driving frequency and voltage were used as control inputs in the speed control. Driving frequency was adjusted by semiconductor switches existing in a two-phase half-bridge serial-resonant inverter circuit controlled by digital integrated circuits, whereas driving voltage was adjusted by a high performance power supply which has full bridge inverter and rectifier circuits controlled by a microcontroller. The drive system designed was tested under several operating parameters in Orcad PSpice 9.1. The simulation results obtained have demonstrated effective achievements for the speed control of TWUSM and so the drive system is convenient for the speed control applications.

A case study on the investigation of solar regime in Van, Turkey

The electricity generation from solar energy is one of the crucial ways for sustainable growth and it is exploited through solar photovoltaic or solar thermal routes for different purposes. However, solar characteristics of a region play a critical role in planning and designing solar power applications. At this point, the solar radiation data is an essential parameter for a proper solar energy analysis. In this context, the solar regime of Van, Turkey is analyzed broadly in this paper. As a distinct approach, not only the solar radiation parameter but also insolation period and air temperature parameters are considered in the conducted analysis here. This meteorological dataset contains hourly, daily and monthly measurements during a six-month period. Many useful visualizations and reasonable assessments are made for the solar characteristics of Van, Turkey.