基于云计算和改进极限学习机的电网负荷预测

随着智能电网和通信技术的迅速发展,电网系统采集的用户数据规模呈指数增长,传统电网负荷预测方法难以满足海量负荷数据情形下的高效分析和计算需求。据此,依托电力系统数据采集云平台,提出一种基于云计算和改进极限学习机的电网负荷预测模型,采用Map-Reduce网络架构,部署于Hadoop平台,利用分布式计算方式进行电网负荷的精准建模和预测分析。结果表明,相比已有方法,本研究方法具有负荷预测精度高、运行速度快的优势,可为后续智能电网系统建设及管理运用提供一种新颖的解决思路。

A Power Grid Load Forecasting Model Utilizing Cloud Computing and Improved Extreme Learning Machine under Massive Data Acquisition

With rapid development of smart grid and communication technology, the scale of user data collected by power grid system has been growing exponentially, and conventional electric power load forecasting algorithms are difficult to deal with the requirement of efficient analysis and calculation in massive data situation. Therefore, based on cloud acquisition platform, we present a power grid load forecasting model combining cloud computing and improved extreme learning machine (ELM). It adopts the Map-Reduce network architecture and is deployed in Hadoop platform, and utilizes the distributed computing technique to carry out accurate modeling and forecasting analysis of power grid load. Experimental results demonstrate that the proposed method has the advantages of higher accuracy and faster operation speed compared with existing methods, which can provide a novel solution for the construction and management of smart grid in the future.