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A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8271-8290页

作者： Lv, Mengzheng Wang, Jianzhou Niu, Xinsong Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Fac Engn & Informat Technol Sch Software Sydney NSW Australia

Accurate implementation of short-term wind speed prediction can not only improve the efficiency of wind power generation, but also relieve the pressure on the power system and improve the stability of the grid. As is known to all, the existing wind speed prediction systems can improve the performance of the prediction in some sense, but at the same time they have some inherent shortcomings, just like forecasting accuracy is not high or indicators are difficult to obtain. In this paper, based on 10-min wind speed data from a wind farm, a new combination model is developed, which consists of three parts: data noise reduction techniques, five artificial single-model prediction algorithms, and multi-objective optimization algorithms. Through detailed and complete experiments and tests, the results demonstrate that the combination model has better performance than other models, solving the problem of instability of traditional forecasting models and filling the gap of low-prediction short-term wind speed forecasting.

关键词： Short-term wind speed forecasting Combination model data denoising technique Multi-objective optimization algorithm

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Wind speed prediction system based on data pre-processing strategy and multi-objective dragonfly optimization algorithm

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 47卷 101346-101346页

作者： Zhang, Linyue Wang, Jianzhou Niu, Xinsong Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Short-term wind speed prediction is an indispensable part of the operation and control of wind energy power generation systems. However, many prediction models proposed by researchers did not preprocess the original data or consider the limitations of a single prediction model, resulting in poor prediction accuracy. Based on the no-negative constraint theory, this study uses five neural networks with advanced optimization algorithms and data preprocessing to obtain high-precision prediction results. Four experiments were designed to test the effectiveness of the proposed model and four analysis strategies were used to discuss the experimental results. The empirical study used wind speed data from China. The results show that the MAPE and Std performance indicators in the multi-step prediction of the hybrid model are lower than in other benchmark models;the proposed model is far superior to comparable models in terms of accuracy and stability.

关键词： Wind speed prediction Neural networks data denoising technique Advanced optimization algorithm

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