A Temporal–Spatial Graph Network With a Learnable Adjacency Matrix for Appliance-Level Electricity Consumption Prediction

Predicting the electricity consumption of individual appliances, known as appliance-level energy consumption (ALEC) prediction, is essential for effective energy management and conservation. Despite its importance, research in this area is limited and faces several challenges: 1) the correlation between the usage of different appliances has rarely been considered for ALEC prediction; 2) a learnable strategy for obtaining the optimal correlation between different appliance behaviors is lacking; and 3) it is difficult to accurately quantify the usage relationship among different appliances. To address these issues, we propose a graph-based temporal–spatial network that employs a learnable adjacency matrix for appliance-level load prediction in this work. The network comprises a temporal graph convolutional network (TGCN) and a learnable adjacency matrix that enables us to utilize correlations between appliances and quantify their relationships. To validate our approach, we compared our model with six others: a TGCN model with a fixed adjacency matrix where all elements are set to 0; a TGCN model with a fixed adjacency matrix where all elements are set to 0.5, except for the diagonal; a TGCN model with a randomly generated adjacency matrix, except for the diagonal; an Aug-LSTM model; a model with ResNetPlus architecture; and a feed-forward deep neural network. Five houses in four datasets: AMPDs, REFIT, UK-DALE, and SC-EDNRR are utilized. The metrics used in this study include root mean square error, explained variance score, mean absolute error, F-norm and coefficient of determination. Our experiments have validated the accuracy and practicality of our proposed approach across different datasets.