Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition.

ABSTRACT: The prediction of energy consumption is of great significance to the stability of the regional energy supply. In previous research on energy consumption forecasting, researchers have constantly proposed improved neural network prediction models or improved machine learning models to predict time series data. Combining the well-performing machine learning model and neural network model in energy consumption prediction, we propose a hybrid model architecture of GRU-MMattention-LightGBM with feature selection based on Prophet decomposition. During the prediction process, first, the prophet features are extracted from the original time series. We select the best LightGBM model in the training set and save the best parameters. Then, the Prophet feature is input to GRU-MMattention for training.

SUBMITTER: Liang S 

PROVIDER: S-EPMC9844920 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature