基于核函数极限学习机的微电网短期负荷预测方法

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摘要针对以单个或集体用户为业主的用户侧小容量微电网,考虑到成本约束及用电特征的不确定性,提出了一种基于核函数极限学习机的微电网短期负荷预测方法。使用核函数极限学习机、启发式遗传算法和分时训练样本,建立了包含离线参数寻优与在线负荷预测的预测模型;通过模型参数的周期更新来保证算法最优参数的时效性,同时降低在线预测系统的计算复杂度与历史数据存储量。通过对不同容量、类型的用户侧微电网进行短期负荷预测,分析了预测结果的准确度、参数周期更新的效果、预测结果对经济运行的影响和预测方法的计算效率。

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	刘念
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	刘海涛

关键词 ：微电网, 短期负荷预测, 极限学习机, 周期更新

Abstract：Considering the cost constraints and various electrical characteristics of the small capacity user-side micro-grid constituted by single or group users, a short-term load forecasting method based on extreme learning machine with kernel(ELM_k) algorithm is proposed. The ELM_k, heuristic genetic algorithm and time division training samples are used to establish a short-term load forecasting model, including offline parameter optimization and online load forecasting. The cycle update of model parameters guarantees the timeliness of the optimum parameters, and reduces the computational complexity and storage space of the online forecasting system. The load forecasting of user-side micro- grids with different capacities and types is processed, and the load forecasting accuracy, the model performance after cycle update, the micro-grid operation costs under load forecasting result and the calculation efficiency of this method are analyzed.

Key words： Micro-grid short-term load forecasting extreme learning machine cycle update

收稿日期: 2013-04-11 出版日期: 2015-05-25

PACS:

TM715

基金资助:国家自然科学基金资助项目（51277067）,中央高校基本科研业务费专项基金项目（12MS32）,国家电网公司科技项目（微电网应用模式与协调控制技术研究开发及应用）

作者简介: 刘念男,1981年生,博士,副教授,研究方向为新能源与智能配用电系统、电力系统信息安全。张清鑫男,1988年生,硕士研究生,研究方向为负荷特征分析、短期负荷预测。

引用本文:

刘念, 张清鑫, 刘海涛. 基于核函数极限学习机的微电网短期负荷预测方法[J]. 电工技术学报, 2015, 30(8): 218-224. Liu Nian, Zhang Qingxin, Liu Haitao. Online Short-Term Load Forecasting Based on ELM with Kernel Algorithm in Micro-Grid Environment. Transactions of China Electrotechnical Society, 2015, 30(8): 218-224.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2015/V30/I8/218

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