A novel ensemble model based on artificial intelligence and mixed-frequency techniques for wind speed forecasting

Wind speed forecasting is of prime importance for wind power generation, which can bring tremendous economic, social and environmental benefits. However, previous wind speed forecasting studies mainly focused on proposing hybrid or ensemble models by combining different methods while ignoring the significant opportunities brought by the era of big data. In particular, the research and applications of mixed-frequency data modeling for wind speed forecasting are almost nonexistent, so some valuable issues remain and need to be studied in depth. In this context, a novel ensemble model, which successfully introduces mixed-frequency data into the field of wind speed forecasting and combines the merits of mixed-frequency models and artificial intelligence methods, is developed for wind speed forecasting. More specifically, the original wind speed data are preprocessed by an advanced data decomposition technique to effectively tackle the challenges caused by noise and capture the main characteristics that existed in the original data. Moreover, some artificial intelligence models and mixed-frequency models are designed as sub-models and employed to predict future wind speed changes; these models aim to make full use of the information existing in common-frequency and mixedfrequency data. Furthermore, a new ensemble forecasting model is developed by combining the power of kernel-based extreme learning machine and multi-objective optimization to ensemble the results output by the sub-models. Various experiments and discussions are designed using four seasons of data, and the proposed model achieves excellent forecasting results. For example, the mean absolute percentage errors of the proposed model are 4.4286%, 6.6154%, 5.2740% and 3.8682% for wind speed forecasting during the four seasons, proving the superiority of the developed model and the innovations and contributions provided by this study from different perspectives.

Automated summary

Wind speed forecasting is crucial for wind power generation, but previous studies focused on model combination and overlooked the opportunities of big data. A novel ensemble model that incorporates mixed-frequency data was developed, achieving excellent forecasting results and making use of the advantages of both mixed-frequency models and artificial intelligence methods.