欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
提出了一种基于果蝇优化算法（FOA）和最小二乘支持向量机（LSSVM）模型的日均电价混合预测模型。将日均电价的历史数据和负荷数据作为输入变量,利用FOA优化选择用于电价预测的LSSVM模型最优参数值,进而对日均电价进行预测。以澳大利亚NSW电力市场的实际数据为例对该模型进行了仿真测试,其结果表明：与自适应LSSVM、模拟退火LSSVM和ARIMA-GARCH模型相比,本文提出的预测模型的预测性能最好,其收敛速度快,预测精度高。
英文摘要：
A hybrid daily average electricity price forecasting model based on fruit flies optimization algorithm（FOA） and least squares support vector machine（LSSVM） model is proposed.The historical data of daily average electricity price and load data are taken as the input variables;the optimal parameter values of LSSVM model are selected by use of FOA;the daily average electricity price is thus forecast.The simulation test based on the actual data of Australia NSW electricity market was performed.The result shows that this proposed model has achieved better forecasting performance due to its faster convergence speed and higher forecasting accuracy compared with self-adaptive LSSVM,simulated annealing LSSVM,and ARIMA-GARCH model.
同期刊论文项目
同项目期刊论文
Photovoltaic power generation in China: Development potential, benefits of energy conservation and e
Examining Economic and Environmental Impacts of Differentiated Pricing on the Energy-Intensive Indus
期刊信息
位置：