Electricity is an important pillar for the economic growth and the development of societies. Surveying and predicting the electricity production (EP) is a valuable factor in the hands of electricity industry managers to make strategic decisions, especially if electricity is generated by renewable resources for environmental considerations. However, because the EP series is non-stationary and nonlinear, traditional methods are less robust to predict it. In this study, we offer a hybrid model which combines adaptive wavelet transform (AWT), long short-term memory (LSTM) and random forest (RF) algorithm (AWT-LSTM-RF) to predict the EP in hydroelectric power plant. We also apply the exogenous affecting variables on EP in the structure of hybrid model, which were selected by ant colony optimization (ACO) algorithm. To evaluate the predictive power of the AWT-LSTM-RF model, we compared its predictive results with the benchmark models including RF, ARIMA-GARCH, wavelet transform-feed forward neural network (WT-FFNN), wavelet transform-random forest (WT-RF), wavelet transform-LSTM (WT-LSTM), and WT-FFNN-RF. The empirical results indicate that the hybrid model of AWT-LSTM-RF outperforms the benchmark models. The results also suggest that applying the wavelet transform on input data of the RF algorithm (WT-RF) can improve the predictive power of the RF.

电力是经济增长和社会发展的重要支柱。对电力生产（EP）进行调查和预测是电力行业管理者做出战略决策的重要因素，特别是如果出于环境考虑，可再生资源产生的电力。但是，由于EP系列是非平稳且非线性的，因此传统方法对其预测的鲁棒性较低。在这项研究中，我们提供了一种混合模型，该模型结合了自适应小波变换（AWT），长短期记忆（LSTM）和随机森林（RF）算法（AWT-LSTM-RF）来预测水力发电厂的EP。我们还将外源影响变量应用于混合模型的结构中的EP，这些变量是通过蚁群优化（ACO）算法选择的。为了评估AWT-LSTM-RF模型的预测能力，我们将其预测结果与基准模型进行了比较，其中包括RF，ARIMA-GARCH，小波变换-前馈神经网络（WT-FFNN），小波变换-随机森林（WT -RF），小波变换-LSTM（WT-LSTM）和WT-FFNN-RF。实证结果表明，AWT-LSTM-RF的混合模型优于基准模型。结果还表明，将小波变换应用于RF算法（WT-RF）的输入数据可以提高RF的预测能力。实证结果表明，AWT-LSTM-RF的混合模型优于基准模型。结果还表明，将小波变换应用于RF算法（WT-RF）的输入数据可以提高RF的预测能力。实证结果表明，AWT-LSTM-RF的混合模型优于基准模型。结果还表明，将小波变换应用于RF算法（WT-RF）的输入数据可以提高RF的预测能力。