RF-GWO-ELM based energy consumption prediction model for the production of prefabricated building components for assembled buildings

The construction of assembled buildings has gradually become an important development direction in the construction industry due to the advantages they offer in terms of efficiency and environmental protection. Nevertheless, the production process of prefabricated components is associated with considerable energy consumption. As the field of assembled buildings has grown at a rapid pace, the management of energy consumption in the production of prefabricated components has become a key area of focus for enterprise efficiency control. Accurate prediction of energy consumption can help manufacturers reduce costs, but the prediction of production energy consumption often suffers from the problems of timeliness and poor accuracy. The study establishes an intelligent energy consumption prediction model, aiming to improve the energy consumption control ability of the process of producing assembled building's prefabricated components, and to enhance the operational efficiency of the enterprise. Random forest is used in the study to lower the dimensionality of the data on manufacturing energy usage. The processed data are fed into the extreme learning machine optimized by the grey wolf optimization algorithm for energy consumption prediction, so as to establish an intelligent energy consumption prediction model. The outcomes indicated that the predictions of the energy consumption prediction model proposed in the study overlapped extremely well with the true values with a root mean square error of 9.2354. Under different production conditions, the mean squared error, mean absolute error, and mean relative error of the research model for predicting the production energy consumption of prefabricated components were 4.7, 2.4, and 0.6, respectively, all of which were better than the comparison model. The study's suggested approach for predicting energy usage increases forecast accuracy for the manufacturing of prefabricated building components and offers businesses scientific decision support.