基于并行膜计算的短期电力负荷组合预测

提出一种并行膜计算(Parallel Membrane Computing，PMC)的电力负荷短期组合预测方法。将线性回归模型、趋势外推模型、改进灰色模型和粒子群优化参数的支持向量机分别放入膜系统的基本膜中，同时并行预测，然后把预测结果输出到表层膜。在表层膜中组合得出最终预测值，组合优化以各种方法预测结果的几何平均数与加权组合结果之差的平方值最小为目标函数，并采用改进粒子群算法分时段优化出权重系数。此外，在进行预测前对历史数据进行了改进滑动平均处理，并采用系统聚类法选出计算输入的历史数据。并行膜计算可以极大地提高组合预测速度，以多种方法预测结果的几何平均数代替真实值确立组合预测模型的目标函数更具实用性。最后，仿真结果验证了所提方法的合理性和有效性。

Short-term power load combination forecasting based on parallel membrane computing

A method of parallel membrane computing (PMC) is proposed to solve the combination of short-term load forecasting. The linear regression model, trend extrapolation model, improved gray model and support vector machines with particle swarm optimization parameters are used to forecast load concurrently in different basic membranes of membrane system, and the prediction results are all output to the surface membrane. In the surface membrane, the ultimate results are got by combined optimization, which is to minimize the square value of geometric mean of above prediction values minus weighted combination result. The weighted coefficient is time-interval optimized by the improved particle swarm method. In addition, historical data has been improved through moving average processing before making the prediction, and can be selected through system clustering method. Parallel membrane computing can greatly improve composition prediction speed. The method that geometric mean of various prediction results replaces real data in objective function has more practicability. Finally, the simulation results show the rationality and effectiveness of the proposed method.