A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting

Accurate wind speed prediction has become increasingly important in wind power generation. However, the lack of efficient data preprocessing techniques and integration strategies has been a big obstacle to the development of wind power forecasting system. Therefore, a novel and advanced combined forecasting system comprising a data preprocessing, an integration strategy and several single models is designed in this study. The proposed model not only eliminates the impact of noise, but also integrates several single-model forecasting results through a weight optimization operator. In addition, the uncertain prediction of wind speed is also discussed in detail. The results show that: (a) The MAPE values of the proposed model are 2.8645%, 2.1843% and 2.8727% respectively for the point prediction. (b) The FICP values of the proposed model are 85.1697, 89.5410 and 88.0111 respectively at the significant level α = 0.05 for the uncertainty forecasting. The AWD values are 0.0559, 0.0400 and 0.0361 and the FINAW values are 0.0478, 0.0404 and 0.0390. It is reasonable to conclude that the proposed system can effectively boost the precision and stability of wind speed forecasting and provide a new approach for the exploitation of wind energy. •A combined forecasting system including a data preprocessing, a combined and uncertainty prediction module is designed.•An advanced data preprocessing technique is intended to remove the noise.•A combined prediction strategy including optimal sub-model selection and weight optimization operators is proposed.•The Pareto optimality of the solutions is theoretically proven.•Interval prediction increases the accuracy and certainty of prediction results.