简介:Animprovedenergydemandforecastingmodelisbuiltbasedontheautoregressivedistributedlag(ARDL)boundstestingapproachandanadaptivegeneticalgorithm(AGA)toobtaincredibleenergydemandforecastingresults.TheARDLboundsanalysisisfirstemployedtoselecttheappropriateinputvariablesoftheenergydemandmodel.Aftertheexistenceofacointegrationrelationshipinthemodelisconfirmed,theAGAisthenemployedtooptimizethecoefficientsofbothlinearandquadraticformswithgrossdomesticproduct,economicstructure,urbanization,andtechnologicalprogressastheinputvariables.Onthebasisofhistoricalannualdatafrom1985to2015,thesimulationresultsindicatethattheproposedmodelhasgreateraccuracyandreliabilitythanconventionaloptimizationmethods.ThepredictedresultsoftheproposedmodelalsodemonstratethatChinawilldemandapproximately4.9,5.6,and6.1billionstandardtonsofcoalequivalentin2020,2025,and2030,respectively.
简介:摘要:针对传统校准方法存在精度不足、操作复杂及耗时长等问题,难以满足现代电力系统对变压器温控器高可靠性、高精度监控的需求。本研究通过构建集标准恒温槽、多路数据采集装置及高精度计算机控制系统于一体的试验平台,实现了对温控器示值误差、回差、接点动作误差、切换差及热模拟附加温升等关键参数的全面自动化校准。采用先进的PID控温技术及PWM电源转换技术,确保恒温槽温度稳定且精确可控。通过优化数据处理流程,引入智能分析算法,自动判断校准数据的有效性,并依据国家规程自动生成校验报告,极大地简化了校准流程,缩短了校准周期。实验结果表明,优化后的校准算法显著提高了变压器温控器的校准精度,降低了人为操作误差,为电力变压器的安全稳定运行提供了有力保障。