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Accurate energy consumption prediction is pivotal in supporting efficient grid scheduling and enhancing renewable energy penetration. This paper proposes a novel hybrid prediction model called CEEMDAN-SE-EC-BiLSTM. The proposed model utilizes Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose the original energy consumption data into Intrinsic Mode Functions (IMFs). Afterwards, Sample Entropy (SE) is employed to evaluate the complexity of the IMFs. The complexed IMFs are then transformed into simpler IMFs using a second decomposition. All IMFs are predicted using Bidirectional Long Short-Term Memory (BiLSTM) to attain the initial predicted sequence. To further refine the prediction, an Error Correction (EC) technique is introduced. This involves subtracting the initial predicted sequence from the original sequence to obtain the error sequence, which is then decomposed using CEEMDAN. Subsequently, the decomposed error sequence is predicted using BiLSTM to generate the error prediction sequence. Finally, the predicted results and the error prediction are combined to obtain the final energy forecast sequence. To verify the superior performance of the proposed method, the model is tested on five selected buildings and benchmarked against seven prediction models. The experimental results indicate that the proposed hybrid prediction model achieves the highest accuracy in building energy prediction. • A novel hybrid approach for building energy prediction is proposed. • The SE is used to evaluate the necessity for secondary decomposition. • The EC technique is employed to improve the prediction accuracy. • The performance of the proposed model is benchmarked against other models. [ABSTRACT FROM AUTHOR]

A novel channel-selection convolutional neural network (CS-CNN) is proposed to predict NOx emission from coal-fired boilers under steady-state and transient load conditions. First, a new channel-selection convolutional layer (CS-CL) is presented to replace regular convolutional layer (RCL). The CS-CL evaluates the channel importance of the input variables, selects the Top-C important channels and releases the hyperparameters of the remaining low-importance channels, thus contributing to maximize the utilization of the parameter resources of the model. The advantages of using CS-CLs are the preservation of the great majority of manipulated variables involved in combustion control among the input variables and the prevention of the model overfitting problem due to the redundancy of input variables. Second, a sliding window-based preprocessing method is applied to the historical data of the boiler which is divided into four-dimensional (4D) tensors. Then, comparative tests are performed on long short-term memory (LSTM) model, baseline CNN and CS-CNN using the historical data of a 670 MW boiler. The results of tests showed that CS-CNN has higher prediction performance. Finally, in order to increase the interpretability of the deep learning black box model, this study analyzes the working mechanism of the CS-CNN through ablation analysis and visualization of model parameters. • Channel importance assessment of input variables by channel selection convolutional layer. • Data-driven model for complete burner manipulation variables. • Novel convolutional neural network for predicting NOx emission from coal-fired boilers. • Model visualization analysis of convolutional neural network for industrial time series data. [ABSTRACT FROM AUTHOR]

Driven by the demand for accurate intrinsic kinetics for the prediction of the combustion rate of purely fired Zhundong coal (ZDC) in a slag tapping boiler, the surface activation function (SAF) and intrinsic kinetic parameters of two types of rapid-pyrolysis ZDC chars were revealed via the isothermal and nonisothermal thermogravimetric analysis methods. The isothermal kinetic results show that the activation energy (E), reaction order and SAF of these two chars were the same, at 146 kJ/mol, 0.78 and X 0.44 (1 – X)−0.48, respectively. Therefore, the difference in the intrinsic reaction rate between these two chars was caused only by the different pre-exponential factors, which are mainly influenced by the specific surface area and catalytic elements in char ash. Nonisothermal kinetic parameters were obtained via the isothermal model-fitting method (ISOM), thereby using the isothermal SAF to fit the nonisothermal data at different heating rates (β s). The results indicate that the E values of the two chars were 148.95 ± 3.12 kJ/mol and 146.16 ± 1.66 kJ/mol, indicating that β has little influence on E. These measured values are consistent with the isothermal E values, so the ISOM is suitable for the determination of the nonisothermal kinetics of rapid-pyrolysis char combustion. • Intrinsic combustion kinetics of rapid-pyrolysis Zhundong coal chars were obtained. • Different chars have the same E , reaction order and surface activation function. • Catalytic elements have a positive effect on A but have little influence on E. • The heating rate has little influence on E of rapid-pyrolysis char combustion. • ISOM is suitable to describe the combustion kinetics of rapid-pyrolysis char. [ABSTRACT FROM AUTHOR]