Wind is one kind of clean and free renewable energy sources. Wind speed plays a pivotal role in the wind power output. However, due to the random and unstable nature of the wind, accurate prediction of wind speed is a particularly challenging task. This paper presents a novel neural fuzzy method for the hourly wind speed prediction. Firstly, a neural structure is proposed for the functional-type single-input-rule-modules（FSIRMs） connected fuzzy inference system（FIS） to combine the merits of both the FSIRMs connected FIS and the neural network. Then, in order to achieve both the smallest training errors and the smallest parameters, a least square method based parameter learning algorithm is presented for the proposed FSIRMs connected neural fuzzy system（FSIRMNFS）. Further,the proposed FSIRMNFS and its parameter learning algorithm are applied to the hourly wind speed prediction. Experiments and comparisons are also made to show the effectiveness and advantages of the proposed approach. Experimental results verified that our study has presented an effective approach for the hourly wind speed prediction. The proposed approach can also be used for the prediction of wind direction, wind power and some other prediction applications in the research field of renewable energy.

This paper presents a data and knowledge driven design approach for the single input rule modules connected fuzzy inference system (SIRM-FIS) which can greatly reduce the number of fuzzy rules. The data and knowledge driven SIRM-FIS can be used to the modeling, identification or prediction problems that have monotonic input-output mappings. In this study, we firstly show how to encode the prior knowledge of monotonicity into the SIRM-FIS. Then, through combining the correlation analysis and the constrained least square algorithm, we present a data-driven parameter learning strategy to optimize the SIRM-FIS. At last, we apply the proposed approach to the thermal comfort prediction. Simulation and comparisons illustrate that the proposed method is efficient to the thermal comfort prediction and performs better than some other existing methods.

Type-2 fuzzy logic systems (T2 FLSs) have shown their superiorities in many real-world applications. With the exponential growth of data, it is a time consuming task to directly design a satisfactory T2 FLS through data-driven methods. This paper presents an ensembling approach based data-driven method to construct T2 FLS through merging type-1 fuzzy logic systems (T1 FLSs) which are generated using the popular ANFIS method. Firstly, T1FLSs are constructed using the ANFIS method based on the sub-data sets. Then, an ensembling approach is proposed to merge the constructed T1 FLSs in order to generate a T2 FLS. Finally, the constructed T2 FLS is applied to a wind speed prediction problem. Simulation and comparison results show that, compared with the well-known BPNN and ANFIS, the proposed method have similar performance but greatly reduced training time.