面向大数据的电力市场分析预测系统设计与应用

发布时间：2019-03-07 22:15

【摘要】：随着电力市场改革在我国的不断深入,电力行业在快速发展的同时,也遇到了新的困难。电力市场改革之后,电力系统中各个市场成员成为了不同的利益主体,经济性被提到了和安全性同样重要的地位。如何尽可能地实现电力系统运行的经济优化?除了在市场规则方面制定相应的措施外,可以考虑通过向各个市场成员提供一些关于市场状况的预测信息,以使整个电力市场安全、稳定、平稳的运行,减少由于对未来市场状况准备不足所带来的对系统正常运行的冲击,同时,也方便各个市场成员合理地决策,谋取利益最大化。本文通过大数据在电力市场预测和分析中的应用研究,为电力市场预测提供建模指导,出具预测趋势计算模型,分析预测期内需电量、负荷及负荷特性和公司售电量等相关指标预测信息,加强规划,运行,营销的信息共享,减少因重复建设造成的成本投入,减少专业部门工作量,有效提升电力市场预测数据处理的及时性、可靠性和准确性,提高数据的使用价值,为公司领导层和管理层提供更准确的预测支撑数据,促进企业业务运作效率的提升,提高电力企业服务社会的工作效率。本文主要从两个方面进行了相关理论方法的创新研究。首先,文章提出了以电力大数据平台为基础构建电力市场分析预测系统,采用以Hadoop为核心的数据采集、分布式存储、分布式处理等大数据生态系统技术,实现数据资源的集中管理、实时监测和可视化管理。其次,本文选取了基于温度变化的居民用电消费习惯主题作为典型数据挖掘应用,实现日最高负荷与温度的关联分析、居民日均用电量与温度的关联分析,掌握负荷随着温度变化的趋势以及城市和农村地区基于温度变化的用电量差异,为有序用电决策和措施提供辅助分析。
[Abstract]:With the deepening of the reform of power market in China, the electric power industry has encountered new difficulties as well as its rapid development. After the reform of the electricity market, the members of the power market have become different stakeholders, and the economy has been mentioned as important as the security. How to realize the economic optimization of power system operation as far as possible? In addition to establishing appropriate measures with regard to market rules, consideration could be given to providing market members with some forecasting information on market conditions in order to ensure the safe, stable and smooth operation of the entire electricity market, It can reduce the impact on the normal operation of the system caused by the lack of preparation for future market conditions, and at the same time, it is convenient for each member of the market to make reasonable decisions and maximize the profits. Through the research of big data's application in forecasting and analysis of electricity market, this paper provides the guidance of modeling for the forecast of electricity market, presents the calculation model of forecasting trend, and analyzes the quantity of electricity demand in the forecast period. Load and load characteristics and the company's electricity sales and other related indicators forecast information, strengthen planning, operation, marketing information sharing, reduce the cost of repeated construction input, reduce the workload of professional departments. Effectively improve the timeliness, reliability and accuracy of forecast data processing in the electricity market, improve the value of data use, provide more accurate prediction support data for the company's leadership and management, and promote the efficiency of business operations. Improve the efficiency of electric power enterprises to serve the society. This article mainly carries on the innovation research of the related theories and methods from two aspects. First of all, this paper puts forward the construction of electricity market analysis and prediction system based on power big data platform, adopting big data ecosystem technology, such as data collection, distributed storage, distributed processing and so on, which is based on Hadoop. Realize the centralized management of data resources, real-time monitoring and visual management. Secondly, the topic of household consumption habits based on temperature change is selected as a typical data mining application to realize the correlation analysis between daily maximum load and temperature, and the correlation analysis between daily average electricity consumption and temperature. The trend of load changing with temperature and the difference of electricity consumption based on temperature change in urban and rural areas are grasped, which can provide auxiliary analysis for orderly decision-making and measures of power consumption.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13;F426.61

【引证文献】