基于周期调整及萤火虫算法优化参数的智能短期负荷预测模型

兰州大学机构库 >数学与统计学院

	基于周期调整及萤火虫算法优化参数的智能短期负荷预测模型
Alternative Title	An intelligent model based on parameter optimized by firefly algorithm with circle adjustment for the short-term power load forecasting
	伍庭波
Thesis Advisor	牛明飞
	2016-05-14
Degree Grantor	兰州大学
Place of Conferral	兰州
Degree Name	硕士
Keyword	短期电力负荷预测快速傅里叶变换萤火虫算法二阶自适应系数
Abstract	短期电力负荷预测通常是指预测一天到一个星期内的电力负荷，它是确保电力调度、电机维护、电力市场有序管理和电力系统安全的关键工作，伴随电力市场的逐步发展和日益完善,准确的短期电力负荷预测成了电力工业健康发展的保障。结合电力负荷序列的特性，本文创造性的构建了一种有效的混合负荷预测模型。混合模型包含的两大模块为快速傅里叶变换结合周期指数调整方法组成的数据预处理方法和萤火虫算法优化二阶自适应系数的预测方法。其中，快速傅里叶变换被用来确定电力负荷数据的周期，再结合周期指数调整方法将原始电力数据转化为周期项和趋势项，再通过萤火虫算法优化二阶自适应系数的方法预测趋势项即为该模型的核心思想。为了评估模型的性能，本文在最后选择新南威尔士、昆士兰和新加坡这三个不同电力市场的半小时电力负荷数据进行数值模拟，根据数值模拟结果显示，该模型对于短期负荷预测有良好的性能。
Other Abstract	Short-time power load forecasting is the guarantee of the healthy development of electric industry, it usually refers to the power load forecasting within a week. Binding characteristics of the power load sequence, the paper creatively construct an effective hybrid load forecasting model. In this paper, the creativity and demonstration of Fast Fourier transform is used to calculate the periodic of the power load data, where the cycle index adjustment is firstly introduced to preprocess the original load data. Meanwhile, the firefly algorithm optimization was presented to optimize the parameters of the Second-order adaptive coefficient method is ground-breaking. Finally, the performance of this proposed novel model is evaluated by one day-ahead load of three different electricity markets from the New South Wales, Queensland and Singapore. Experimental results shown that this novel model has a good performance by comparison with other previous approach.
URL	查看原文
Language	中文
Document Type	学位论文
Identifier	https://ir.lzu.edu.cn/handle/262010/225072
Collection	数学与统计学院
Recommended Citation GB/T 7714	伍庭波. 基于周期调整及萤火虫算法优化参数的智能短期负荷预测模型[D]. 兰州. 兰州大学,2016.

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