| 引用本文: | 李 楠,姜 涛,隋 想,胡禹先.一种时频尺度下的多元短期电力负荷组合预测方法[J].电力系统保护与控制,2024,52(13):47-58.[点击复制] |
| LI Nan,JIANG Tao,SUI Xiang,HU Yuxian.A multi-component short-term power load combination forecasting method on a time-frequency scale[J].Power System Protection and Control,2024,52(13):47-58[点击复制] |
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| 摘要: |
| 随机因素的增加导致电力负荷数据成分日渐复杂，使短期负荷预测的难度逐渐增大。针对该问题，提出一种时频尺度下的时间卷积网络与多元线性回归相融合的组合预测模型。利用自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)在时频域上将负荷数据分解为若干个频率特征不同的本征模态分量，在模糊熵准则下聚类为随机项和趋势项。采用皮尔逊系数从诸多影响因素中筛选出与电力负荷高度相关的特征，鉴于小时间尺度分析更易于挖掘局部细节特征，分别构建了随机项与趋势项的细颗粒度特征集。利用具有强非线性处理能力的时间卷积网络(temporal convolutional network, TCN)去预测随机项，利用结构简单及线性拟合效果好的多元线性回归(multiple linear regression, MLR)去预测趋势项，将二者的预测结果进行叠加重构后获得最终预测值。在新加坡和比利时两组数据集上的实验结果证明：所提模型具有较高的预测精度、较好的泛化性能及鲁棒性。 |
| 关键词: 短期电力负荷预测 时频尺度 分解算法 模糊熵 模型融合 |
| DOI：10.19783/j.cnki.pspc.231284 |
| 投稿时间：2023-10-03修订日期：2023-11-18 |
| 基金项目:国家自然科学基金项目资助(61973072) |
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| A multi-component short-term power load combination forecasting method on a time-frequency scale |
| LI Nan1,2,JIANG Tao2,SUI Xiang3,HU Yuxian4 |
| (1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of
Education (Northeast Electric Power University), Jilin 132012, China; 2. School of Electrical Engineering, Northeast
Electric Power University, Jilin 132012, China; 3. Nanjing Nari-Relays Electric Co., Ltd., Nanjing 210000, China;
4. Baicheng Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Baicheng 137000, China) |
| Abstract: |
| The increase of stochastic factors leads to increasing complexity of power load data components. This makes short-term load forecasting progressively more difficult. Thus a combined forecasting model fusing a temporal convolutional network with multiple linear regression on the time-frequency scale is proposed. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the load data into multiple intrinsic mode functions with different frequency features in the time-frequency domain, and the intrinsic mode functions are clustered into random and trend terms under the fuzzy entropy criterion. The Pearson correlation coefficient is used to pick out features that are highly relevant to the power load from many influential factors. The analysis of a small time scale makes it easier to determine local detailed features, and the fine granularity feature set of the random and trend terms are constructed respectively. The temporal convolutional network (TCN) with strong nonlinear processing ability is used to predict the random term, and the multiple linear regression (MLR) with simple structure and good linear fitting effect is used to predict the trend term. The final predicted value is obtained by superposing and reconstructing both predicted results. Experimental results on two datasets including for Singapore and Belgium prove that the proposed model has high prediction accuracy, good generalizability and robustness. |
| Key words: short-term power load forecasting time-frequency scale decomposition algorithm fuzzy entropy model fusion |
