SHORT-TO MEDIUM-TERM WIND POWER PREDICTION BASED ON IMPROVED DEEP FUZZY NEURAL NETWORK

Li Lanqing; Li Yan

doi:10.19912/j.0254-0096.tynxb.2025-0410

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 197-204. DOI: 10.19912/j.0254-0096.tynxb.2025-0410

SHORT-TO MEDIUM-TERM WIND POWER PREDICTION BASED ON IMPROVED DEEP FUZZY NEURAL NETWORK

Li Lanqing, Li Yan

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Abstract

A short-to medium-term wind power prediction model based on deep fuzzy neural network（DFNN） is proposed. Firstly, the fuzzy rules and the number of rules are determined by introducing an effectiveness function to construct an adaptive fuzzy C-means clustering algorithm, reducing the impact of manually setting the number of clusters on the clustering performance of traditional fuzzy C-means clustering algorithms; Secondly, an improved quantum genetic algorithm is adopted to optimize the consequent layer parameters of the deep fuzzy neural network. This algorithm increases the diversity of gene collapse results and global optimization ability by controlling the numerical range of adaptive dynamic rotation angle during the initial iteration; Finally, the proposed model was applied to short-term and medium-term wind power prediction, with wind speed, wind direction, and environmental temperature as inputs and predicted power as outputs. The four models were tested, and the results showed that the proposed model had high accuracy in short-term wind power prediction during the selected time periods in spring and summer, as well as on the medium-term time scale, verifying the feasibility of the method.

Key words

wind power / power prediction / deep fuzzy neural network / quantum genetic algorithm / fuzzy clustering / deep learning

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Li Lanqing, Li Yan. SHORT-TO MEDIUM-TERM WIND POWER PREDICTION BASED ON IMPROVED DEEP FUZZY NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 197-204 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0410

References

[1] 兑红炎, 张雨露, 刘朝, 等. 基于重要度的风力发电系统可靠性优化分析[J]. 运筹与管理, 2023, 32(12): 15-21.
Dui H Y, Zhang Y L, Liu C, et al.Importance measure-based reliability optimization of a wind power system[J]. Operations Research and Management Science, 2023, 32(12): 15-21.
[2] 孙荣富, 张涛, 和青, 等. 风电功率预测关键技术及应用综述[J]. 高电压技术, 2021, 47(4): 1129-1143.
Sun R F, Zhang T, He Q, et al.Review on key technologies and applications in wind power forecasting[J]. High Voltage Engineering, 2021, 47(4): 1129-1143.
[3] 蒋建东, 孙书凯, 董存, 等. 风电中长期电量预测研究现状[J]. 高电压技术, 2022, 48(2): 409-419.
Jiang J D, Sun S K, Dong C, et al.Research status of mid-long term wind power generation forecasting[J]. High Voltage Engineering, 2022, 48(2): 409-419.
[4] 王士同, 谢润山, 周尔昊. 可解释的深度TSK模糊系统综述[J]. 数据采集与处理, 2022, 37(5): 935-951.
Wang S T, Xie R S, Zhou E H.Survey of interpretable deep TSK fuzzy systems[J]. Journal of Data Acquisition and Processing, 2022, 37(5): 935-951.
[5] Raju G V S, Zhou J, Kisner R A. Hierarchical fuzzy control[J]. International Journal of Control, 1991, 54(5): 1201-1216.
[6] Sun C L, Li H T.Parallel fuzzy relation matrix factorization towards algebraic formulation, universal approximation and interpretability of MIMO hierarchical fuzzy systems[J]. Fuzzy Sets and Systems, 2022, 450: 68-86.
[7] Alateeq M, Pedrycz W.Logic-oriented fuzzy neural networks: a survey[J]. Expert Systems with Applications, 2024, 257: 125120.
[8] Öztürk H T.Research on optimal solutions and algorithm stability analyses in RC continuous beam problems[J]. Structures, 2024, 62: 106239.
[9] 潘海鹏, 丁海波, 雷美珍, 等. 基于模糊逻辑的直驱式海浪发电系统最大功率跟踪控制[J]. 太阳能学报, 2021, 42(6): 21-28.
Pan H P, Ding H B, Lei M Z, et al.Maximum power tracking control of direct drive ocean wave power system based on fuzzy logic[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 21-28.
[10] 吴永忠, 刘华威, 侯诗文, 等. 模拟退火遗传算法在风力提水机翼型优化设计中的研究[J]. 太阳能学报, 2021, 42(6): 385-390.
Wu Y Z, Liu H W, Hou S W, et al.Research on simulated annealing genetic algorithm in optimization design of water-pimping wind-mill[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 385-390.
[11] 高博, 刘建兴, 孙浩, 等. 基于自适应引力搜索算法的定日镜场优化布置[J]. 太阳能学报, 2022, 43(10): 119-125.
Gao B, Liu J X, Sun H, et al.Optimization of a heliostat field layout using adaptive gravity search algorithm[J]. Acta Energiae Solaris Sinica, 2022, 43(10): 119-125.
[12] 吴玲, 张秀锦, 刘秋华, 等. 基于多元宇宙优化算法的光伏发电MPPT控制算法[J]. 太阳能学报, 2023, 44(9): 204-211.
Wu L, Zhang X J, Liu Q H, et al.Mppt control algorithm of photovoltaic power generation based on multi-verse optimization algorithm[J]. Acta Energiae Solaris Sinica, 2023, 44(9): 204-211.
[13] 李千禧, 何武全, 李渤, 等. 基于混合和谐搜索算法的自压滴灌田间管网多目标优化设计[J]. 节水灌溉, 2024(6): 40-45.
Li Q X, He W Q, Li B, et al.Multi-objective optimization design of self-pressure drip irrigation field pipe network based on mixed harmony search algorithm[J]. Water Saving Irrigation, 2024(6): 40-45.
[14] Zhang J H, Xiao M, Gao L, et al.Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems[J]. Applied Mathematical Modelling, 2018, 63: 464-490.
[15] Shi Y H.Brain storm optimization algorithm[C]//Advances in Swarm Intelligence. 2011: 303-309.
[16] 庞传军, 余建明, 张波, 等. 基于偏依赖量的风功率影响因素相关性分析方法[J]. 电网技术, 2021, 45(2): 552-558.
Pang C J, Yu J M, Zhang B, et al.Correlation analysis of factors affecting wind power based on partial dependence[J]. Power System Technology, 2021, 45(2): 552-558.
[17] 乔楠, 蒋波涛, 郑雨, 等. 基于深度模糊神经网络的太阳总辐射预测研究[J]. 太阳能学报, 2024, 45(2): 59-64.
Qiao N, Jiang B T, Zheng Y, et al.Research on global solar radiation forecast based on deep fuzzy neural network[J]. Acta Energiae Solaris Sinica, 2024, 45(2): 59-64.