Forecasting of System Marginal Price of Electricity Using General Regression Neural Network
In the electric market system marginal price (SMP) can help the company accept a fitness bidding strategy and yield good economic returns. But it is difficult to be forecasted because of its complexity and uncertainty. A general regression neural network was proposed to forecast SMP because its foundation of probability conforms to SMPs uncertainty. The key of smoothing parameter was optimized by an improved particle swarm optimization method and three main factors of electrical load, historical corresponding hour SMP value and current SMP tendency were considered as independent variables. The simulation from actual data showed this method is effective.
Electric market System marginal price General regression neural network Particle swarm optimization Forecasting
LIN Zhiling JIA Mingxing
College of Information Science and Engineering Northeast University Shenyang, 110004, China
国际会议
厦门
英文
768-771
2006-07-27（万方平台首次上网日期，不代表论文的发表时间）