A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm - Wang, Jianzhou; Li, Hongmin; Wang, Ying; Lu, Haiyan (Expert systems with applications, 2021)
•A novel hesitant fuzzy wind speed forecasting system is proposed for the first time.•Multi-fuzzification methods are proposed to deal with the non-determinism problem.•The weights of intervals are determined by multi-objective optimization algorithm.•A new defuzzification model is developed to obtain accurate and reliable forecasts.•The proposed system outperforms comparison models with high accuracy and efficiency. Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency.