Research on the Prediction of Enterprise Energy Consumption Based on VMD-GWO-HKELM
Energy consumption prediction is the major concern of an enterprise and is one of the important tasks for an enterprise to establish an energy consumption management and control platform,which plays a significant role in ensuring safe and stable energy consumption.An energy consumption prediction model of optimized hybrid kernel extreme learning machine(HKELM)is proposed based on variational mode decomposition(VMD)and grey wolf optimizer(GWO).First,VMD is employed to decompose energy consumption data sequence and obtain sub-sequences with different characteristic rules,so as to reduce the randomness of raw data of energy consumption.Then,Gaussian kernel function and polynomial kernel function are adopted to establish HKELM model with a larger generalization ability.Meanwhile,considering the difficulty in the selection of parameters of HKELM model,GWO is employed to optimize the selection of parameters and establish GWO-HKELM model.Finally,the data after decomposition are input into GWO-HKELM model,and the prediction results of all sub-sequences are added together to obtain the final prediction result.The data of actual electric energy consumption of a plant in Zhejiang Province is used as an example to verify the effectiveness and feasibility of such model.
energy consumption predictionenergy consumption management and controlVMDGWOHKELMdata decompositionGaussian kernel functionpolynomial kernel function
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