双碳目标下的煤炭价格预测与预警研究

针对“双碳”目标背景下能源安全供应需要煤电兜底保供的问题，对影响煤电生产保供的煤炭价格进行了预测研究。首先，建立了基于布谷鸟搜索算法优化的长短期记忆网络（CS-LSTM）煤炭价格预测模型。模型运用布谷鸟搜索算法对LSTM的学习率和隐藏层神经元个数2个参数进行寻优，完成了参数确定，加强了LSTM的预测能力。其次，建立了煤炭价格预警机制，对煤炭价格的波动做出警示。最后，基于CS-LSTM模型预测了2022年山西电煤价格指数，同时进行了价格预警。实例计算结果验证了所提模型预测精准度和预警机制的有效性。

Coal Price Prediction and Early Warning Under Carbon Peak & Carbon Neutrality Goals

In order to address the need for coal to underpin energy security under carbon peak & carbon neutrality goals，predictive study of coal prices that affect coal production and supply is presented. Firstly， a long and short-term memory network based on optimized cuckoo search algorithm（CS-LSTM） coal prices forecasting model is set up . The model employs the cuckoo search algorithm to find the optimum for two parameters of the LSTM，namely the learning rate and the number of neurons in the hidden layer，to complete the parameter determination and strengthen the forecasting capability of the LSTM. Secondly，an early warning mechanism for coal prices is established to warn the fluctuations in coal prices. Finally， Shanxi coal price index in 2022 based on CS-LSTM model is predicted， and price warning is provided. The results of the example calculations verify the prediction accuracy of proposed forecasting model and the effectiveness of the early warning mechanism.