Data driven hybrid fuzzy model for short-term traffic flow prediction

A novel purification machine and fuzzy inference method based hybrid model for wind speed forecasting

As one kind of readily available renewable energy sources, wind is widely used in power generation where wind speed plays an important role. Generally speaking, we need to forecast the wind speed for improving the controllability of wind power generation. However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance. On one hand, we propose the purification machine which utilize the Irregular Information Reduction Module (IIRM) and the Irrelevant Variable Reduction Module (IVRM) to reduce the randomness and instabilities of the data and to eliminate the variables with zero or negative effect in the wind speed time series. On the other hand, we adopt the developed Single-Input-Rule-Modules based Fuzzy Inference System (SIRM-FIS), the functionally weighted SIRM-FIS (FWSIRM-FIS) to realize the prediction of wind speed. This FWSIRM-FIS utilizes the multi-variable functional weights to dynamically measure the importance of the input variables so that the input-output mapping can be strengthened and more accurate forecasting results can be achieved. Furthermore, detailed experiments and comparisons are given. Experimental results demonstrate that the proposed FWSIRM-FIS and purification machine contributes greatly to deal with the randomness and instability in the wind speed data and yield more accurate forecasting results than those existing excellent forecasting models.