1) annual electric consumption
年用电量
1.
Using support vector regression based on clonal selection algorithm in annual electric consumption forecasting;
基于克隆选择算法的支持向量回归实现年用电量预测
2) annual electricity consumption
年度用电量
1.
To forecast the annual electricity consumption of ultra-exponent increase trend,linearizing mathematical functions,such as ultra-factorial and duplicate-exponent,were adopted to directly fit the historical loads and forecast the future annual loads.
为有效预测超过指数增长速度的年度用电量,选用超阶乘、二重指数等可线性化的函数,对年度用电量进行曲线直接拟合外推预测。
4) Annual consumption volume
年用量
5) annual water consumption
年用水量
1.
Based on demonstration of selection of index system and concentration multiplying power, calculation of annual water consumption and repeated use factor the paper indicates that some indexes to be offered for comparison should be set first and investigation and analysis should be according to the practical conditions of individual power plant.
通过对有关《取水定额》的指标体系、浓缩倍率选择、年用水量计算和重复利用率的论述 ,说明 :应该设置一些可供比较的指标 ,但不能片面地“指标挂帅” ;要从每个电厂的实际情况出发 ,充分进行调研和分析 ,因地制宜地采用合理的节水措施和耗水指标。
6) annual power generation
年发电量
1.
Through analysis of the specialty of annual power generation series of small hydropower stations,a regression model based on the least squares support vector machine(LS-SVM) was introduced into the field of annual energy production forecasting in this paper.
针对小水电站年发电量序列的特点,将最小二乘支持向量机(least squares support vector machine,LS-SVM)回归模型引入年发电量预测领域,并给出了相应的过程和算法。
补充资料：农村用电量
农村用电量：指在本年度内，农村生产上和生活上的全年用电总度数（全年累计数）。但不包括农村中的全民所有制工业、交通、基建单位的用电量，也不包括县办工业和城镇生活用电量。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条