电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2013年
1期
70-75
,共6页
邹云峰%罗欣%田诺%赵燃%李婧娇
鄒雲峰%囉訢%田諾%趙燃%李婧嬌
추운봉%라흔%전낙%조연%리청교
供电服务中心%短期话务量预测%分层相似法%逐时降水%特征话务
供電服務中心%短期話務量預測%分層相似法%逐時降水%特徵話務
공전복무중심%단기화무량예측%분층상사법%축시강수%특정화무
power supply service center%short-term traffic forecasting%layered similar forecasting method%hourly precipitation data%traffic quantity
供电服务中心话务量受到多种因素影响.首先分析了供电服务中心话务水平和话务曲线特性,确定工作日与周六、周日星期类型对话务量的影响.在此基础上分析温度、降水对话务量的影响,确定平均温度、最低温度和逐时降水量是影响话务量的关键因素,尤其是降水量是影响话务曲线突变的重要指标.考虑到降水日和非降水日的区别,提出了分层相似预测法,即将话务曲线分成基础话务曲线和特征话务值,对于非降水日,基础话务曲线即为预测曲线,对于降水日,将特征话务量值进行叠加形成预测曲线.实例验证了所提方法的正确性和有效性.所做工作对提高供电服务中心话务量预测精度,指导中心优化排班具有重要现实意义和实用价值.
供電服務中心話務量受到多種因素影響.首先分析瞭供電服務中心話務水平和話務麯線特性,確定工作日與週六、週日星期類型對話務量的影響.在此基礎上分析溫度、降水對話務量的影響,確定平均溫度、最低溫度和逐時降水量是影響話務量的關鍵因素,尤其是降水量是影響話務麯線突變的重要指標.攷慮到降水日和非降水日的區彆,提齣瞭分層相似預測法,即將話務麯線分成基礎話務麯線和特徵話務值,對于非降水日,基礎話務麯線即為預測麯線,對于降水日,將特徵話務量值進行疊加形成預測麯線.實例驗證瞭所提方法的正確性和有效性.所做工作對提高供電服務中心話務量預測精度,指導中心優化排班具有重要現實意義和實用價值.
공전복무중심화무량수도다충인소영향.수선분석료공전복무중심화무수평화화무곡선특성,학정공작일여주륙、주일성기류형대화무량적영향.재차기출상분석온도、강수대화무량적영향,학정평균온도、최저온도화축시강수량시영향화무량적관건인소,우기시강수량시영향화무곡선돌변적중요지표.고필도강수일화비강수일적구별,제출료분층상사예측법,즉장화무곡선분성기출화무곡선화특정화무치,대우비강수일,기출화무곡선즉위예측곡선,대우강수일,장특정화무량치진행첩가형성예측곡선.실례험증료소제방법적정학성화유효성.소주공작대제고공전복무중심화무량예측정도,지도중심우화배반구유중요현실의의화실용개치.
There are many factors which influence the telephone traffic of power supply service center. This paper firstly analyzes the level and the curve shape of the traffic to prove the effect of the type of weeks. Then this paper determines the average temperature, minimum temperature and hourly precipitation data are the other key factors of the development and change of the traffic, especially precipitation is the most important indicator of the curve mutation. Taking into account the difference between the precipitation days and non-precipitation days, the paper proposes a layered similar forecasting method which divides the forecasting result into basic traffic curves and traffic quantity. The basic curve is the predicted result for non-precipitation days, and the traffic quantity and basic traffic curves are superimposed to form the prediction result for precipitation days. A case is given to demonstrate the effectiveness of the method described. The paper’s work can improve traffic forecasting accuracy of power supply service center, which has significant applicable value for optimized scheduling.