Details

Autor(en) / Beteiligte

• Heng, Jiani
• Wang, Chen
• Zhao, Xuejing
• Xiao, Liye

Titel

Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

Ist Teil von

• Sustainability (Basel, Switzerland), 2016-03, Vol.8 (3), p.235-235

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2016

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid [k, Bt] -ABBP (back propagation based on adaptive strategy with parameters k and Bt) model was developed based on an adaptive boosting (AB) strategy that integrates several BP (back propagation) neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm) incorporating a conjugate gradient (CG) is proposed for searching for the optimal parameters of the [k, Bt] -ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.