Multi-Equipment energy consumption prediction based on improved STGCN
In order to help enterprises realize energy saving of high energy consumption equipment.Most existing researches on equipment load forecasting focus on single equipment and give priority to the time characteristics of data,but lack the analysis of spatial characteristics.This paper a multi-equipment energy consumption prediction method based on Spatio-Temporal Graph Convolution Neural Network(STGCN)is proposed.The Chebyshev Graph Convolution(Cheb-GC)layer and the improved Gated Temporal Convolu-tion Network(GTCN)are used to extract spatial features and temporal features.Through embedding graph adjacency matrix,this method achieves the purpose of multi-equipment joint prediction.Combined with the real data set,the proposed model is compared with the Historical Average(HA)regression model,Autore-gressive Integrated Moving Average(ARIMA)model,Feedforward Neural Network(FNN)and Gated Re-current Unit(GRU)network.The results show that this algorithm has better performance,and the predic-tion results meet the requirements of practical application.
multi-equipment energy consumption predictionSpatio-Temporal characteristicsGraph Neu-ral NetworkData Miningdeep learning
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