A novel combined model for wind speed prediction - Combination of linear model, shallow neural networks, and deep learning approaches

Accurate wind speed forecasting is increasingly essential for improving the operating efficiency of electric power systems. Numerous models have been proposed to obtain the accurate and stable wind speed forecasting results. However, previous proposed models are limited by single predictive model or cannot deal with complex nonlinear data characteristic, which resulted in poor and unstable prediction results. In this paper, a novel forecasting model that combines noise processing, statistical approaches, deep learning frameworks and multi-objective optimization algorithm is proposed. Multi-objective optimization algorithms can take advantage of the merits of benchmark prediction models to address nonlinear characteristics of wind speed series. The 10-min real wind speed data from three Sites in China are adopted for verifying the effectiveness of this proposed model. The experimental results of multi-step prediction show that the model achieves MAPE(1-step) = 2.2109%, MAPE(2-step) = 3.0309%, and MAPE(3-step) = 4.2536% at Site 1; MAPE(1-step) = 2.4586%, MAPE(2-step) = 3.2034%, and MAPE(3-step) = 4.6843% at Site 2; MAPE(1-step) = 2.3180%, MAPE(2-step) = 3.0846%, and MAPE(3-step) = 4.4193% at Site 3. Therefore, the forecasting performance of this model is excellent, and it is beneficial to the dispatching and planning of power grid. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a novel wind speed forecasting model that combines noise processing, statistical approaches, deep learning frameworks, and multi-objective optimization algorithms. Experimental results from three sites in China demonstrate excellent forecasting performance of the proposed model.