This research offers a fast and accurate method for measuring the biogas production rate throughout biogas production. An agricultural biogas plant's measurement of eight process variables served as the source of ...
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This research offers a fast and accurate method for measuring the biogas production rate throughout biogas production. An agricultural biogas plant's measurement of eight process variables served as the source of experimental data used to create the models. Biomass type, reactor/feeding, volatile solids, pH, organic load rate, hydraulic retention time, temperature, and reactor volume were utilized in this context. Artificial neural networks (ANN) were developed to evaluate the biogas production rate. The variable selection was carried out using the cuckoo optimizationalgorithm (COA), multi-verse optimization algorithm (MVO), leagues championship algorithm (LCA), evaporation-rate water cycle algorithm (ERWCA), stochastic fractal search (SFS), and teaching-learning-based optimization (TLBO). In this study, the model's size decreased, the important process variables were highlighted, and the ANN models' potential was enhanced for prediction. The proposed COA, MVO, LCA, ERWCA, SFS, and TLBO and ensembles are the outcome of using the abovementioned approaches to synthesize the multi-layer perceptron (MLP). To evaluate the effectiveness of the used models, we have developed a scoring system in addition to employing mean absolute error, mean square error, and coefficient of determination as accuracy criteria. Implementing the COA, MVO, LCA, ERWCA, SFS, and TLBO algorithms enhances the accuracy of the MLP. It is found that some of the used hybrid techniques could provide better prediction outputs than traditional MLP rankings. Additional investigation indicated that the ERWCA is better than the three other algorithms. The biogas production rate was estimated with the greatest precision with R2 = 0.9314 and 0.9302, RMSE of 0.1969 and 0.24925, and MAE of 0.1307 and 0.19591.
Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy...
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Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimizationalgorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053 %, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption.
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