基于PSO-SVM的电网调度电厂耗煤基准值滚动预测

电网调度在做电煤供应分级预警的研究制定和对各电厂负荷经济分配时常会用到耗煤基准值，它对于监测电厂未来存煤使用天数，制定合理发电调度计划具有重要作用。目前常用的耗煤基准测算方法很少，主要是通过对历史数据进行简单的数学计算作为未来耗煤基准预测值，误差较大。提出了结合实际需求、面向电网调度的基于粒子群优化支持向量机的火电厂耗煤基准值滚动预测方法，选取电网下典型的3个电厂，经过数据分析处理与试验比对，结果表明基于粒子群优化支持向量机模型能很好地对耗煤基准值进行滚动测试及预测更新，进而可以为电网调度部门推测各个电厂存煤可用天数和建立电厂存煤预警机制，制定发电调度计划提供数据支撑。

PSO-SVM-Based Rolling Forecast of Coal Consumption Reference Value for the Power Plants Dispatched by Power Grid

The power grid dispatcher often uses the coal consumption reference value when making the research plan of the graded early warning system for coal-fired supply or the economic load distribution schedule for each power plant. It plays an important role in both monitoring the coal stockpiles in terms of days of burn in the power plant and formulating a reasonable power generation dispatch schedule. However, there are only few methods applicable so far, most of which simply process the historical data into the mathematical calculation as the forecast value for future coal consumption benchmark and hence may introduce considerable computation errors. By taking account of the actual demand, a method using the rolling calculation of unit coal consumption benchmark value is proposed on the basis of particle swarm optimization support vector machine for power grid scheduling.Three typical power plants in the power grid are selected in the case studies. Through detailed data analysis and comparison testing, from the results the support vector machine model based on particle swarm optimization has demonstrated satisfactory performance in rolling test and forecast update on coal consumption reference value, which means it can even provide further data support for the grid dispatch department to estimate the number of days of coal storage available, establish a coal storage early warning mechanism and formulate a power generation dispatch plan.