| 徐 浩,刘青红,任 正,张 爽.基于融合技术的中长期电力负荷预测方法[J].电力需求侧管理,2024,26(4):94-99 |
| 基于融合技术的中长期电力负荷预测方法 |
| Mid-long term electricity load forecasting method based on fusion techniques |
| 投稿时间：2024-04-06 修订日期：2024-06-30 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 04. 015 |
| 中文关键词: 中长期电力负荷预测 深度因果卷积神经网络 变分自编码器 灰色预测 空间负荷密度预测 融合技术 |
| 英文关键词: mid-long term power load forecasting deep causal convolutional neural network variational auto-encoder grey forecasting spatial load density prediction fusion techniques |
| 基金项目:内蒙古自治区科技重大专项（2021ZD0039） |
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| 中文摘要: |
| 当前电力负荷预测模型在数据复杂性高、数据稀缺、模型泛化和动态社会经济因素适应性方面存在局限,影响了其在复杂电网规划中的应用。为满足电网或者大型风、光、火、储、网、荷能源基地项目的规划调度需求,提出了一种融合技术,将灰色预测、空间负荷密度预测和变分自编码器与深度因果卷积神经网络相结合,以实现中长期负荷预测。通过引入有序加权平均微分算子,融合不同预测方法,提升结果的准确性。实验结果表明,本方法相较于传统方法展现更高的准确性和鲁棒性,特别是在进行电力负荷远景预测时,所提方法能够有效提升预测的可靠性和适用性。该技术有效克服传统方法固有的数据复杂性、数据稀缺性和模型泛化问题,同时适应社会经济条件的动态变化。该方法为电网、大型源网荷储多能互补类项目的规划和发展提供有力的决策支持。 |
| 英文摘要: |
| Current electrical load forecasting models are constrained by high data complexity, data scarcity, limited generalization, and in?sufficient adaptability to dynamic socio-economic factors, impeding their utility in sophisticated grid planning. To meet the planning andscheduling requirements of power grids or large-scale wind, solar, thermal, storage, grid, and load energy base projects, an integrated tech?nology has been proposed. Grey forecasting, spatial load density forecasting, variational autoencoders, and deep causal convolutional neu?ral networks are combined for medium to long-term load forecasting. The introduction of an ordered weighted averaging differential opera?tor amalgamates various predictive techniques, thereby refining accuracy. The experimental results demonstrate that the proposed methodexhibits higher accuracy and robustness compared to traditional methods, particularly in the context of long-term electric load forecasting,effectively enhancing the reliability and applicability of the predictions. This technology effectively overcomes issues of data complexity,data scarcity and model generalization inherent in conventional methods, while adjusting to socio-economic dynamics. It provides substan?tial decision-making support for the planning and evolution of power networks and large-scale integrated energy projects. |
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