Abstract:

Aiming at the problem that the global energy consumption prediction model is only suitable for part of the prediction sample and the model is computationally intensive, the idea of just-in-time learning is introduced, and the local weighted partial least squares method combined with the energy consumption model is used to establish a temporary local energy consumption prediction model. The inertia weights of the particle swarm algorithm are improved, considering the effects of particle fitness, number of iterations and population size on the convergence speed and convergence accuracy of the particle swarm algorithm, a nonlinear change adaptive inertia weight strategy is proposed, and the improved adaptive PSO(APSO) is used to optimize the bandwidth parameters of historical samples in the offline computing stage, then the local model is updated online when the predicted samples are available. Considering the prediction error caused by the different energy consumption of the samples under different working conditions in multi-working condition production scenarios, andincreasing the measurement process of working condition similarity, an APSO-JITL-CLWPLS energy consumption prediction modeling method combining local weighted partial least squares algorithm and K-means algorithm is proposed, and the bandwidth parameters of the predicted samples are designed by selecting the historical samples of the same working conditions during prediction. Simulation experiments show that the algorithm has higher prediction accuracy and can better cope with the multi-working production scenarios.

Key words: just-in-time learning, locally weighted partial least squares, clustering, online modeling, multi-working conditions, bandwidth parameters, energy consumption