| 刘永建,张雯璇.基于CGAN-BiGRU的初创企业用电负荷预测[J].电力需求侧管理,2026,28(2):51-56 |
| 基于CGAN-BiGRU的初创企业用电负荷预测 |
| Electricity load forecasting for start-ups based on CGAN-BiGRU |
| 投稿时间：2025-11-15 修订日期：2026-01-12 |
| DOI：10.3969/j.issn.1009-1831.2026.02.008 |
| 中文关键词: 初创企业 用电负荷预测 条件生成对抗网络 双向门控循环单元 |
| 英文关键词: start up electricity load forecasting conditional generative adversarial networks bidirectional gated recurrent unit |
| 基金项目:国家自然科学基金资助项目(72271123) |
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| 中文摘要: |
| 针对初创企业因历史数据匮乏、生产计划波动而导致用电负荷难以准确预测的难题,用电模式不稳定的难题,提出了基于条件生成对抗网络(conditional generative adversarial networks,CGAN)和双向门控循环单元(bidirectional gated recurrent unit,Bi-GRU)的初创企业用电负荷预测方法。首先,在分析影响企业用电的关键内外部因素的基础上,形成结构化的影响因素体系,构建包括配电数据、设备能耗、生产计划和气象环境的多变量特征。然后,融合多变量特征与用电负荷时序数据构建原始样本,并利用CGAN实现数据增强。最后,将增强后的样本输入到BiGRU网络进行训练,以捕捉复杂的双向时序依赖关系,实现用电负荷预测。以某航空发动机维修初创企业为案例的实验表明,该方法在数据稀缺条件下仍能保持高精度,其预测误差较基线模型显著降低,有效验证了所提数据与特征增强策略的优越性。该研究可为数据不足场景下的负荷预测提供理论和应用支撑。 |
| 英文摘要: |
| To address the challenge of inaccurate electricity load forecasting in start-up enterprises caused by insufficient historical data, highly fluctuating production plans, and unstable electricity consumption patterns, an electricity load forecasting method for start-up enterprises based on conditional generative adversarial networks (CGAN) and bidirectional gated recurrent units (BiGRU) is proposed. Firstly, on the basis of an analysis of key internal and external factors affecting enterprise electricity consumption, a structured influencing-factor system is established, and a multivariate feature set including power distribution data, equipment energy consumption, production planning information, and meteorological conditions is constructed. Subsequently, multivariate features are fused with electricity load time-series data to form original samples, and data augmentation is implemented using CGAN. Finally, the augmented samples are fed into a BiGRU network for training, enabling the capture of complex bidirectional temporal dependencies and the realization of electricity load forecasting. Experimental results based on a case study of an aircraft engine maintenance start-up enterprise demonstrate that high prediction accuracy can be maintained under data-scarce conditions, with prediction errors significantly reduced compared to baseline models, thereby effectively validating the superiority of the proposed data and feature enhancement strategy. Theoretical and practical support is provided for electricity load forecasting in data-limited scenarios. |
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