Rock mass rating system （RMR） is based on the six parameters which was defined by Bieniawski （1989） [1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rou...Rock mass rating system （RMR） is based on the six parameters which was defined by Bieniawski （1989） [1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rough calculation. As a result, there is a sharp transition between two modules which create doubts. So, in this paper the proposed weights technique was applied for linguistic criteria. Then by using the fuzzy inference system and the multi-variable regression analysis, the accurate RMR is predicted. Before the performing of regression analysis, sensitivity analysis was applied for each of Bieniawski parameters. In this process, the best function was selected among linear, logarithmic, exponential and inverse func- tions and finally it was applied in the regression analysis for construction of a predictive equation. From the constructed regression equation the relative importance of the input parameters can also be observed. It should be noted that joint condition was identified as the most important effective parameter upon RMR. Finally, fuzzy and regression models were validated with the test datasets and it was found that the fuzzy model predicts more accurately RMR than reression models.展开更多
A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is ...A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. In this paper, an SVR （support vector regression） using FCM （Fuzzy C-Means） is proposed for wind speed forecasting. This paper describes the design of an FCM based SVR to increase the prediction accuracy. Proposed model was compared with ordinary SVR model using balanced and unbalanced test data. Also, multi-step ahead forecasting result was compared. Kernel parameters in SVR are adaptively determined in order to improve forecasting accuracy. An illustrative example is given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.展开更多
文摘Rock mass rating system （RMR） is based on the six parameters which was defined by Bieniawski （1989） [1]. Experts frequently relate joint and discontinuities and ground water conditions in linguistic terms with rough calculation. As a result, there is a sharp transition between two modules which create doubts. So, in this paper the proposed weights technique was applied for linguistic criteria. Then by using the fuzzy inference system and the multi-variable regression analysis, the accurate RMR is predicted. Before the performing of regression analysis, sensitivity analysis was applied for each of Bieniawski parameters. In this process, the best function was selected among linear, logarithmic, exponential and inverse func- tions and finally it was applied in the regression analysis for construction of a predictive equation. From the constructed regression equation the relative importance of the input parameters can also be observed. It should be noted that joint condition was identified as the most important effective parameter upon RMR. Finally, fuzzy and regression models were validated with the test datasets and it was found that the fuzzy model predicts more accurately RMR than reression models.
文摘A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. In this paper, an SVR （support vector regression） using FCM （Fuzzy C-Means） is proposed for wind speed forecasting. This paper describes the design of an FCM based SVR to increase the prediction accuracy. Proposed model was compared with ordinary SVR model using balanced and unbalanced test data. Also, multi-step ahead forecasting result was compared. Kernel parameters in SVR are adaptively determined in order to improve forecasting accuracy. An illustrative example is given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.
