基于FCGA和改进LSTM-BPNN的燃气负荷预测

Gas Load Forecasting Using FCGA and Improved LSTM-BPNN

准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用.由于燃气负荷数据本身具有周期性,随机性的复杂特点以及单阶段单预测模型的局限性,本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms,FCGA)和改进的LSTM-BPNN残差修正模型的多阶段混合模型.首先第一阶段先用LSTM进行燃气负荷初步预测,然后计算出燃气负荷残差值,第二阶段先用BPNN去预测残差值,然后用Adam自适应学习率算法在学习过程中自动调节LSTM-BPNN残差模型的学习率,加快拟合速度,接着用模糊编码遗传算法去优化BPNN的初始权重和阈值,以便寻找到全局最优解.最后把两阶段的预测值和作为最终的燃气负荷预测值.通过对比实验得出,本文模型比单模型,原始两阶段预测模型得到了更高的预测准确率.

The accurate forecasting of daily natural gas load is pivotal to the reasonable supply and dispatch of energy in the city.This study proposes a multistage hybrid model based on Fuzzy Coding of Genetic Algorithms(FCGA)and the improved LSTM-BPNN residual correction model since gas load data is periodic but random and the single-stage and single-forecasting model has a limited role.In the first stage,the gas load is forecasted by the LSTM model to calculate its residual value.In the second stage,the residual value is predicted by the BPNN model,and then the learning rate of the LSTM-BPNN residual model is automatically adjusted with the Adam algorithm regarding the adaptive learning rate to accelerate fitting.Afterward,the initial weights and thresholds of the BPNN are optimized by the fuzzy coding of genetic algorithms to find the global optimal solution.Finally,the sum of the forecasting values in the two stages is taken as the final gas load.Comparative experiments prove that the model in this study ensures higher prediction accuracy than the single model and the original two-stage forecasting model.