Prediction Method for Peak Carbon Emissions in Power Systems Based on Improved Grey Neural Network

To address the practical challenges of poor data quality, strong hyperparameter coupling, and significant peak positioning errors in power system carbon emission peak forecasting, a framework integrating robust data preprocessing with an improved grey-convolution hybrid model is proposed. Firstly, a dynamic quantile boundary-based outlier detection and multi-window weighted robust repair procedure is established, together with a random forest feature importance-based chained multiple imputation method, to suppress outlier disturbances and high-dimensional missingness in non-Gaussian data. Subsequently, an improved convolutional network incorporating variational mode decomposition, dilated convolution, and attention gating is constructed, with an embedded improved grey model for long-term trend extraction; hyperparameter optimization is achieved through a grey relational grade-guided whale optimization algorithm. Experimental results show that compared with genetic algorithm, particle swarm optimization, and grey wolf optimization, the proposed algorithm reduces mean absolute percentage error by 39.7%, 32.5%, and 25.4%, and peak prediction time deviation by 77.1%, 71.4%, and 60.0%, respectively. Compared with autoregressive integrated moving average (ARIMA), long short-term memory - prophet (LSTM-Prophet), time-series transformer (TST), empirical mode decomposition-LSTM (EMDE-LSTM), and variational mode decomposition - gated recurrent unit (VMD-GRU), the proposed model reduces mean absolute percentage error to 2.89% and peak prediction time deviation to 0.7 h, while improving the inflection point capture rate to 93.8%. This study provides a new technical approach for accurate carbon emission peak prediction and offers data support for power system emission reduction strategy formulation.