• Energy Research

Abstract Fast-growing carbon emissions from the residential building sector are a hindrance for China to achieve its 2030 emission peak goal. To identify future low carbon roadmaps of residential buildings, this study is the first to assess the historical carbon mitigation and simulate the energy and emission peaks of China’s residential building sector using a dynamic emission scenario. It shows that the emission mitigation of the residential building sector during 2000–2015 is 1.817 (±0.643) billion tons of carbon dioxide (BtCO2), and the normal distribution-based scenario simulation demonstrates that the residential building sector will achieve its carbon emission peak in 2037 (±4) with a peak value of 1.419 (±0.081) BtCO2. The sensitivity analysis reveals that the impacts of floor space per capita and energy intensity of urban residential buildings are the most significant for the uncertainty of emission peaks. A strict energy demand benchmark of the residential building sector suggests a control at 0.661 billion tons of standard coal equivalent (Btce), and its peaking time is estimated for 2035, which is 5 years ahead of the business-as-usual scenario, with energy savings of 0.042 Btce. For the earliest peaking time, if the residential building sector aims to achieve its emission peak before 2030, the emission peak should be controlled at 1.258 BtCO2. Overall, this paper can assist the government in more accurate and feasible building emission mitigation strategies. Moreover, the results provide a more powerful decision-making reference in issuing targeted and feasible strategies for future residential building emission mitigation.

Abstract Introduction of the fractional order accumulation has made significant contributions to the development of forecasting methods, and fractional grey models play a key role in such new methods. However, the fractional grey models may also be inaccurate in some cases as they do not consider the time delayed effect. To further improve the applicability of the existing fractional grey models, a novel fractional grey model called the fractional time delayed grey model is proposed in this paper. The essence of the fractional time delayed term is discussed, revealing that the fractional time delayed term is essentially a function between the polynomial functions with integer order, which can be more flexible to improve the modelling accuracy. The cutting-edge Grey Wolf Optimizer is introduced to find the optimal value of fractional order. Detailed modelling procedures, including the computational steps and the intelligent optimization algorithm, have been clearly presented. Four real world case studies are used to validate the effectiveness of the proposed model, in comparison with 8 existing grey models. Finally the proposed model is applied to forecast the coal and natural gas consumption of Chongqing China, the results show that the proposed model significantly outperforms the other 8 existing grey models.

Abstract The grey models have appealed considerable interest of research due to their effectiveness for time series forecasting with small samples. But most of the existing grey models are essentially linear models, which limits the applicability of the grey models. In this paper, we introduce a novel nonlinear multivariate grey model which is based on the kernel method, and named as the kernel-based GM(1, n), abbreviated as the KGM(1, n). The KGM(1, n) model contains an unknown function of the input series, which can be estimated using the kernel function, and then the KGM(1, n) model is available to describe the nonlinear relationship between the input and output series. The case studies of predicting the oilfield production, the condensate gas well production and coal gas emission are carried out, and the results show that the KGM(1, n) model is much more efficient than the existing linear multivariate grey models and the LSSVM. The nonlinearity of KGM(1, n), the effects of the data structure, the sample size and the prediction term on the KGM(1, n) model have also been discussed combined with the theoretical analysis and the numerical experiments.

AbstractCompressor stations, which usually consist of multiple compressors in parallel, are installed to power natural gas travel in pipelines. Compressor station optimization, which should be expressed as a mixed integer nonlinear programming (MINLP) problem, makes economic sense for the entire gas transmission system. However, it has often been simplified as a nonlinear programming (NLP) or mixed integer linear programming (MILP) problem in previous research. Most of existing solutions are based on discretization and a genetic algorithm (GA). This paper addresses the general MINLP problem for compressor station optimization without simplification; a novel hybrid intelligent algorithm is proposed to solve this problem. The proposed algorithm, DWOA, leverages advantages of the whale optimization algorithm (WOA) and differential evolution (DE). The proposed algorithm can balance exploration and exploitation to find the global optimal solution. An approach to handling constraints is also presented, where the original problem model is reformulated to be continuous by expanding the flow rate range of the compressor. A case study is performed to illustrate the performance of this approach. Results show that the continuous reformulated model is easier to solve, and DWOA produces a satisfactory solution that differs from theoretical results by only 1.61%. In addition, DWOA demonstrates better accuracy and stability than WOA, DE, and DE‐WOA, another hybrid algorithm. Therefore, this solution has the potential to promote comprehensive compressor station optimization.

Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model’s performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model’s prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting.

Abstract The accurate prediction of energy price is critical to the energy market orientation, and it can provide a reference for policymakers and market participants. In practice, energy prices are affected by external factors, and their accurate prediction is challenging. This paper provides a systematic decade review of data-driven models for energy price prediction. Energy prices include four types: natural gas, crude oil, electricity, and carbon. Through the screening, 171 publications are reviewed in detail from the aspects of the basic model, the data cleaning method, and optimizer. Publishing time, model structure, prediction accuracy, prediction horizon, and input variables for energy price prediction are discussed. The main contributions and findings of this paper are as follows: (1) basic prediction models for energy price, data cleaning methods, and optimizers are classified and described; (2) the structure of the prediction model is finely classified, and it is inferred that the hybrid model and prediction architecture with multiple techniques are the focus of research and the development direction in the future; (3) root mean square error, mean absolute percentage error, and mean absolute error are the three most frequently used error indicators, and the maximum mean absolute percentage error is less than 0.2; (4) the ranges of data size and data division ratio for energy price prediction in different horizons are given, the proportion of the test set is usually in the range of 0.05–0.35; (5) the input variables for energy price prediction are summarized; (6) the data cleaning method has a more significant role in improving the accuracy of energy price prediction than the optimizer.

Abstract Fractional order accumulation is a novel and popular tool which is efficient to improve accuracy of the grey models. However, most existing grey models with fractional order accumulation are all developed on the conventional methodology of grey models, which may be inaccurate in the applications. In this paper an existing fractional multivariate grey model with convolution integral is proved to be a biased model, and then a novel fractional discrete multivariate grey model based on discrete modelling technique is proposed, which is proved to be an unbiased model with mathematical analysis and stochastic testing. An algorithm based on the Grey Wolf Optimizer is introduced to optimize the fractional order of the proposed model. Four real world case studies with updated data sets are executed to assess the effectiveness of the proposed model in comparison with nine existing multivariate grey models. The results show that the Grey Wolf Optimizer-based algorithm is very efficient to optimize the fractional order of the proposed model, and the proposed model outperforms other nine models in the all the real world case studies.