Optimization of Extreme Learning Machine for Air-conditioning Load Prediction Based on Improved Dingo Optimization Algorithm
A novel air conditioning load prediction model based on Microbial genetic algorithm(MGA)and Dingo optimization algorithm(DOA)optimized Extreme learning machine(ELM)is proposed in this paper to address the issues of low prediction accuracy and poor stability in short-term air conditioning load prediction methods.A DOA-ELM prediction model is established by using DOA to optimize the input weights and hidden layer thresholds of ELM.An MDOA-ELM prediction model is established by using MGA to improve the prediction stability and accuracy of the DOA-ELM model.To reduce the dimensionality of the prediction model,Grey relational analysis(GRA)is used to screen the input and output factors that affect air conditioning load.An air conditioning load prediction example on the central air conditioning system of a factory is provided to verify the effectiveness of the proposed algorithm.Comparing with the reported model,the experimental results show that the established load prediction model has higher prediction accuracy and better stability,and therefore is able to better meet the actual needs of the project.
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