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1、设计（论文）内容及要求：一、设计内容：1. 了解EMS系统相关知识2. 确定预测目标、搜集与整理资料3. 对电力系统短期负荷预测进行较为系统的研究4. 分析资料，选择预测方法5. 确定短期负荷预测方法6. 建立短期负荷预测模型7. 对短期负荷预测进行仿真实验研究8. 进行预测分析二、设计要求：1. 翻译该课题相关英文论文一篇2. 设计说明书一份（含中英文摘要、正文、程序清单）三、 参考资料：1.能量管理系统2.电力系统自动化等有关电力系统负荷预测方面的参考文献3. 有关MATLAB/SIMULINK仿真方面的教材及资料4.神经网络技术5.智能控制理论6.电力系统短期负荷预测指导教师： 年 月
2、日本科生毕业设计(论文)开题报告设计(论文)题目基于灰色理论的短期电力负荷预测设计(论文)题目来源自选题目设计(论文)题目类型理论设计起止时间2007.12.132008.6.1一、设计(论文)依据及研究意义：依据：电力负荷预测对于保证电力工业的健康发展，乃至整个国民经济的发展均有着十分重要的意义。负荷预测依其运用领域可分为：运转规划、电源开发及电力系统规划等三种，其不同应用领域所需负荷预测之内容亦不尽相同。因此，负荷预测的模式及其所使用的数学模式与公式，皆随各电力事业不同背景与环境的条件而有相当大的差异。意义：准确的负荷预测，可以避免经济合理的安排电网内部发电机组的启停，保持电网运行的安全稳
3、定性，减少不必要的旋转储备容量，合理安排机组检修计划，保证社会的正常生产和生活，有效地降低发电成本，提高经济效益和社会效益。二、设计(论文)主要研究的内容、预期目标：(技术方案、路线)内容：1、进行系统分析2、建立灰色系统模型GM 模型即灰色模型（GREY MODEL），一般来说，建模是用原始的数据序列建立差分方程；灰色系统建模则是用原始数据序列作生成数后建立微分方程。由于系统被噪音污染后，所以原始数据序列呈现出离乱的情况，这种离乱的数列也是一种灰色数列，或者灰色过程，对灰色过程建立模型，便成为灰色模型。3、运用灰色理论进行负荷预测灰色系统理论研究的是贫信息下建模,提供了贫信息下解决系统问题的
4、新途径.它把一切随机过程看作是在一定范围内变化的,是与时间有关的灰色过程.对灰色量不是从统计规律的角度应用大样本进行研究,而是采用数据生成的方法,将杂乱无章的原始数据整理成规律性强的生成序列再作研究.4、预测误差分析在对试算结果进行统计分析中发现：预测日负荷值时，如果同时出现气候温度突变的情况，预测准确率也会下降。对此，我们决定：根据气候温度的突变程度分出几个不同的调整权重，温度以28为分界，低于28每相差4为一档，高于28每相差2为一档。预期目标： 2007 .12 完成翻译 2008.3收集资料2008.5建模2008.6编程三、设计(论文)的研究重点及难点：重点：建立灰色模型及其改进模型
5、难点：灰色模型的数学建模及其MATLAB程序的编写四、设计(论文)研究方法及步骤(进度安排)：设计研究方法：以定性分析为主步骤：1、确定负荷预测目的，制订预测计划 2、搜寻、整理、分析资料 3、建立预测模型、运用MATLAB软件编程及仿真 4、确定预测结果，分析误差 5、编写预测报告五、进行设计(论文)所需条件：1电力系统短期负荷预测样本数据（某市2003年11月电力负荷实际数据、该市2003年11月天气情况的数据） 有关负荷预测和灰色理论的期刊和书籍、MATLAB软件六、指导教师意见： 签名： 年 月 日LOAD FORECASTINGEugene A. FeinbergState Univ
6、ersity of New York, Stony BrookEugene.Feinbergsunysb.eduDora GenethliouState University of New York, Stony Brookdgenethlams.sunysb.eduAbstract Load forecasting is vitally important for the electric industry in the deregulated economy. It has many applications including energy purchasing and generati
7、on, load switching, contract evaluation, and infrastructure development. A large variety of mathematical methods have been developed for load forecasting. In this chapter we discuss various approaches to load forecasting.Keywords: Load, forecasting, statistics, regression, artificial intelligence.1.
8、 IntroductionAccurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructur
9、edevelopment. Load forecasts are extremely important for energy suppliers,ISOs, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets.Load forecasts can be divided into three categories: short-term forecasts which are usually from one h
10、our to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year. The forecasts for different time horizons are important for different operations within a utility company. The natures of these forecasts are different as well. For exampl
11、e, for a particular region, it is possible to predict the next day load with an accuracy ofapproximately 1-3%. However, it is impossible to predict the next year peak load with the similar accuracy since accurate long-term weather forecasts are not available. For the next year peak forecast, it is p
12、ossible to provide the probability distribution of the load based on historical weather observations. It is also possible, according to the industry practice,to predict the so-called weather normalized load, which would take place for average annual peak weather conditions or worse than average peak
13、 weather conditions for a given area. Weather normalized load is the load calculated for the so-called normal weather conditions which arethe average of the weather characteristics for the peak historical loads over a certain period of time. The duration of this period varies from one utility to ano
14、ther. Most companies take the last 25-30 years of data.Load forecasting has always been important for planning and operationaldecision conducted by utility companies. However, with the deregulation of the energy industries, load forecasting is even more important.With supply and demand fluctuating a
15、nd the changes of weather conditions and energy prices increasing by a factor of ten or more during peaksituations, load forecasting is vitally important for utilities. Short-term load forecasting can help to estimate load flows and to make decisions that can prevent overloading. Timely implementations of such decisions lead to the improvement of network reliability and to the reduced occurrences of equipment failures and blackouts. Load forecasting is also important for contr
