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Accurate prediction of energy prices is crucial to the development of energy security and environmental policies in various countries. This paper proposes a novel multi-step prediction hybrid model with genetic algorithm for variational mode decomposition, improved complete ensemble empirical modal decomposition with adaptive noise, bidirectional gated recurrent unit, temporal convolutional network, and multi-layer perceptron (GVMD-ICEEMDAN-BIGRU-TCN-MLP) for predicting carbon and natural gas futures prices. First genetic algorithm (GA) is used to fix the parameters of VMD model, the carbon and natural gas prices are decomposed into subsequences. Then the difference between the original series and the VMD after decomposition is further decomposed into subseries using ICEEMDAN. Next, the highest frequency series is predicted using the MLP model, and other subsequences are predicted using the BIGRU-TCN model. Finally, each predicted value is added linearly to determine the final result of steps 1, 3, and 5 of the entire forecasting process. According to the experimental results, it is shown that the model has lower prediction errors than the comparison model under mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and modified Diebold-Mariano test (MDM). The good prediction results of the novel hybrid model are demonstrated in multi-step ahead integrated prediction experiments, especially in the experiments with 1-step ahead prediction, as well as in the experiments with varying training ratios. • GVMD-ICEEMDAN-BIGRU-TCN-MLP model is used to forecast energy prices. • ICEEMDAN is used to further decompose the residuals. • Hierarchical prediction based on subsequence features is better. • Multi-step forecasting integrated study of energy prices. [ABSTRACT FROM AUTHOR]

Allam cycle is an advanced semi-closed supercritical carbon dioxide (sCO 2) power cycle that utilizes hydrocarbon fuels with nearly zero carbon emission. Different from other closed power cycles, in the Allam cycle, the oxy-fuel combustion products are directly mixed with the working fluid and the turbine blade cooling is necessary for the high cycle temperature above 1000 °C; moreover, the heat integration might be applied to the Allam cycle to improve the thermodynamic performance, making the Allam cycle complex and hard to conduct the process and parameter optimization. To deepen the understanding on the performance of the Allam cycle with turbine cooling and recompression modification, a novel splitting analytical method is developed that the Allam cycle is split into the closed cycle and open process, and the closed cycle is further split into the simple host cycle and two equivalent power cycles. Using the constructed splitting analytical models, the influences of the turbine cooling and the recompression modification on the net efficiency are clearly evaluated by the split cycles, and the highest efficiency of the Allam cycle is optimized to be 49.92 % (higher heating value) as the coolant temperature and recompression mass flow rate are 320 °C and 439.5 kg/s, respectively. • The Allam cycle is split into the closed cycle and open process. • The closed cycle is further split into several simple cycles. • Turbine cooling and recompression are analyzed and optimized. • The highest net efficiency soars to 49.92 % based on higher heating value. [ABSTRACT FROM AUTHOR]