| 引用本文: | 郭耀扬,张 利,郝 颖,等.基于分行业用电特性与多因素影响的区域级短期用电负荷曲线预测[J].电力系统保护与控制,2025,53(13):82-92.[点击复制] |
| GUO Yaoyang,ZHANG Li,HAO Ying,et al.Regional short-term electricity load curve forecasting based on industry electricity consumption characteristics and multi-factor effects[J].Power System Protection and Control,2025,53(13):82-92[点击复制] |
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| 摘要: |
| 针对“双碳”目标推进下，区域级全社会用电负荷曲线预测精度低的问题，提出了一种基于分行业用电特性与多因素影响的全社会用电负荷曲线预测框架。首先，结合行业负荷曲线聚类分析结果、综合用电评价指标构建方法，从定性和定量两方面对不同行业用电特性进行分析，验证分行业精细化用电特性挖掘的必要性。然后，采用非线性相关系数、小提琴图可视化分析方法，量化不同季节气温、日类型对不同行业用电特性的影响程度，为后续分行业精细化预测奠定数据基础。最后，利用卷积神经网络(convolutional neural network, CNN)提取行业用电特性，结合双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)与注意力机制(attention mechanism, Attention)构建集成预测模型，精细化预测各行业负荷曲线，采用间接预测方式实现全社会用电负荷曲线预测。结合华东某区域十一个行业以及居民用电负荷数据，设置10组实验进行对比分析，结果表明所提预测框架较传统预测方法能够显著降低预测误差。 |
| 关键词: 行业用电特性 综合用电评价指标 小提琴图 负荷曲线预测 |
| DOI：10.19783/j.cnki.pspc.241291 |
| 投稿时间：2024-09-24修订日期：2025-01-20 |
| 基金项目:国家电网公司总部科技项目资助(5108-202218280A-2-379-XG) |
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| Regional short-term electricity load curve forecasting based on industry electricity consumption characteristics and multi-factor effects |
| GUO Yaoyang1,ZHANG Li1,HAO Ying2,ZHAO Bo1,ZHOU Ying3,MA Xiaotian4,LI Chuang1 |
| (1. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China; 2. Tangshan Research
Institute, Beijing Institute of Technology, Tangshan 063000, China; 3. Beijing Key Laboratory of Demand Side Multi-energy
Carriers Optimization and Interaction Technique (China Electric Power Research Institute), Beijing 100192, China;
4. State Grid Hebei Marketing Service Center, Shijiazhuang 050035, China) |
| Abstract: |
| Aiming at the problem of low accuracy of regional level total electricity load curve forecasting under the “dual carbon” goals, a forecasting framework that incorporates industry-specific electricity consumption characteristics and multi-factor influences is proposed. First, industry load curve clustering analysis and the construction of comprehensive electricity consumption evaluation indicators are used to qualitatively and quantitatively analyze the electricity consumption characteristics of different industries, demonstrating the necessity of refined, industry-level load profiling. Furthermore, the impact of various external factors, such as seasonal temperatures and day types, on industry-specific electricity usage patterns is quantified using nonlinear correlation coefficients and violin plot visualizations, laying a data foundation for fine-grained forecasting. Finally, a hybrid forecasting model is constructed by extracting electricity usage features via convolutional neural network (CNN), integrating them with the bi-directional long short-term memory (BiLSTM) network and the attention mechanism. This ensemble model is used to predict load curves for each industry, and an indirect forecasting approach is applied to reconstruct the total electricity load curve of the entire society. Using load data from eleven industries and residential electricity users in a region of East China, ten groups of comparative experiments are conducted. Results show that the proposed forecasting framework significantly reduces the prediction errors compared to traditional methods. |
| Key words: industry electricity consumption characteristics comprehensive electricity consumption evaluation index violin diagram load curve forecasting |
