考虑多因素气象的电网短期负荷预测建模研究

发布时间：2018-07-16 15:54

【摘要】：短期负荷预测(Short-term load forecasting,STLF)是对未来若干小时、1天至几天的电力负荷预报,作为安排发购电计划,经济分配负荷及安排机组出力的基础,精准的负荷预测是保证电网安全可靠运行的前提条件。随着居民生活水平的提高,能源消耗加大,调温负荷占总用电负荷的比重日益增长,导致电网气象敏感负荷不断上升,从而构成用电峰荷,拉大了电网峰谷差,现有的短期负荷预测技术在应对复杂气象条件时预测精度难以满足电网要求。为落实电网对负荷精细化管理的要求,进一步提高电网负荷预测的精细化水平,确保电网安全稳定运行,研究能真实反映负荷变化规律的负荷预测模型,对于提高短期负荷预测精度十分有必要。自迈入电力大数据时代以来,系统原始运行数据的存量增加,电力负荷预测技术与相关科学领域技术,如气象、经济等的交叉渗透。不可置否,大数据将是未来电网的生产力,因此在短期负荷预测领域深度挖掘气象、负荷大数据的价值,是融合大能源思维与大数据思维研究考虑多因素气象的负荷预测建模,实现电力负荷精细化管理,提高短期负荷预测精度不可或缺的一部分。本文在电力大数据的基础上,本文首先分析了考虑多因素气象的负荷特性,从年周期、季周期、日周期等时间维度以及气象的特殊性方面剖析了气象对负荷的影响。针对气象变化时负荷曲线预测精度低,预测模型不能完全适应气象变化的情况,本文提出了一种提出了完全气象因子序列的概念,基于数据挖掘的方法建立气象粒化集;采用空间多元回归及滞后模型结合多策略灵敏度分析法,建立了针对复杂气象条件下的曲线拐点预测模型;基于改进的K-means聚类分析法查找并获取气象特征日,计算初步预测曲线,主动判断预测曲线畸变概率并进行优化修正,得到最佳预测负荷曲线。为应对气象突变对负荷曲线的影响提出了基于多粒度气象信息匹配的曲线修正模型,针对突变气象进行曲线修正。最后利用动态数据流对模型参数进行更新,实现精细化预测。最后采用本文方法对我国南方某地区全年负荷曲线进行预测,验证了模型在多种气象条件下的预测准确性,尤其适用于短期内气象存在复杂变化的情形。
[Abstract]:Short-term load forecasting (STLF) is a power load forecast for the next few hours or days, which serves as the basis for arranging generation and purchase plans, economic load distribution and generating units. Accurate load forecasting is the precondition to ensure the safe and reliable operation of power grid. With the improvement of residents' living standard, energy consumption is increasing, and the proportion of temperature adjustment load to total power load is increasing day by day, which leads to the rising of meteorological sensitive load of power grid, which forms the peak load of electricity consumption and widens the difference between peak and valley of power grid. The existing short-term load forecasting technology is difficult to meet the requirements of power grid when dealing with complex meteorological conditions. In order to meet the requirement of fine load management, to improve the precision of load forecasting, to ensure the safe and stable operation of power network, a load forecasting model which can truly reflect the law of load change is studied. It is necessary to improve the accuracy of short-term load forecasting. Since entering the era of electric power big data, the stock of the original operation data of the system has increased, and the interpenetration of power load forecasting technology and related scientific fields, such as meteorology, economy, etc. Big data will be the productivity of power grid in the future. Therefore, in the field of short-term load forecasting, the value of load big data is a combination of large energy thinking and big data thinking research, considering multi-factor meteorological load forecasting modeling. It is an indispensable part to realize the fine management of power load and improve the precision of short-term load forecasting. Based on the big data of electric power, this paper first analyzes the load characteristics of multi-factor meteorology, and analyzes the influence of meteorology on the load from the time dimension of annual cycle, season period, daily period and the particularity of meteorology. Because the forecasting precision of load curve is low and the forecasting model can not adapt to the situation of meteorological change, a concept of complete meteorological factor series is put forward in this paper, and the meteorological granulation set is established based on data mining method. By using spatial multivariate regression and lag model combined with multi-strategy sensitivity analysis, the curve inflection point prediction model for complex meteorological conditions is established, and the weather feature days are found and obtained based on improved K-means clustering analysis. The preliminary prediction curve is calculated, the distortion probability of the prediction curve is judged and the optimal load forecasting curve is obtained. In order to deal with the influence of meteorological catastrophe on load curve, a curve correction model based on multi-granularity meteorological information matching is proposed. Finally, the dynamic data stream is used to update the model parameters to achieve fine prediction. Finally, the method of this paper is used to forecast the annual load curve in a certain area of southern China, which verifies the accuracy of the model under various meteorological conditions, especially in the case of complex meteorological changes in the short term.
【学位授予单位】：广西大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM715

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