| 引用本文: | 刘云,张杭,张爱民.需求侧响应下基于负荷特性的改进短期负荷预测方法[J].电力系统保护与控制,2018,46(13):126-133.[点击复制] |
| LIU Yun,ZHANG Hang,ZHANG Aimin.Improved load forecasting method based on load characteristics under demand-side response[J].Power System Protection and Control,2018,46(13):126-133[点击复制] |
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| 摘要: |
| 为了提高需求侧电力负荷预测精度，针对需求侧自身特点，提出了基于负荷特性的改进短期负荷预测方法。依据需求侧负荷特性与属性聚类算法结合的方法完成两级需求侧负荷分类，并使用优化改进粒子群优化径向基神经网络(MPSO-RBF)和最小二乘支持向量机回归模型(LS-SVM)等算法建立短期预测模型进行负荷预测。利用该方法对某工业园区用电负荷进行预测，并与实际用电负荷数据和利用传统预测模型以及单一模型预测方法进行了比较分析。预测结果平均相对误差表明，基于负荷特性的改进短期负荷预测方法是有效和实用的，既能得到准确的负荷预测结果，方便需求侧用户就地进行各类负荷针对性调控，又方便管理者宏观掌控需求侧用户负荷情况，有效推动能源互联网的发展。 |
| 关键词: 需求侧响应 属性聚类 改进粒子群优化径向基神经网络 最小二乘支持向量机 短期负荷预测 |
| DOI：10.7667/PSPC170167 |
| 投稿时间：2017-02-10修订日期：2017-11-02 |
| 基金项目: |
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| Improved load forecasting method based on load characteristics under demand-side response |
| LIU Yun,ZHANG Hang,ZHANG Aimin |
| (School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China) |
| Abstract: |
| In order to improve the accuracy of load forecasting for the demand side, this paper proposes an improved short-term load forecasting method based on demand-side load characteristics. It completes the two-segment classification for demand-side load according to the load characteristic and the attribute clustering algorithm, then establishes the forecasting model for load forecasting by using MPSO-RBF and LS-SVM. This method is used to forecast the power load of an industrial park, and is compared with the actual load data, the traditional forecasting model and the single model prediction method. It shows that the method is effective and practical, it can get accurate prediction results and control load for the users pertinently, and it is convenient to macroly control the load aggregation for manager. What’s more, it can promote the development of the energy internet effectively. |
| Key words: demand side load attribute cluster MPSO-RBF LS-SVM short-term prediction |
