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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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

Received: 11 April 2013 Published: 25 May 2015

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TM715

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	Liu Nian
	Zhang Qingxin
	Liu Haitao

Cite this article:

Liu Nian,Zhang Qingxin,Liu Haitao. Online Short-Term Load Forecasting Based on ELM with Kernel Algorithm in Micro-Grid Environment[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 218-224.

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

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