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

• Published in: PloS one Vol. 18; no. 1; p. e0277085
• Main Authors: Liang, Shaokun, Deng, Tao, Huang, Anna, Liu, Ningxian, Jiang, Xuchu
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.01.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Biology and Life Sciences; Computer and Information Sciences; Decision trees; Decomposition; Energy consumption; Energy research; Engineering and Technology; Feature extraction; Forecasting; Forecasts and trends; Growth models; Learning algorithms; Machine Learning; Methods; Modelling; Mutation; Neural networks; Neural Networks, Computer; Noise reduction; Physical Phenomena; Physical Sciences; Prediction models; Regression analysis; Research and Analysis Methods; Seasons; Stability; Time series; Time-series analysis; Training; Windows (intervals); China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0277085

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. Finally, MLP is used to learn the final prediction weight between LightGBM and GRU-MMattention. After the prediction weights are learned, the final prediction result is determined. The innovation of this paper lies in that we propose a structure to learn the internal correlation between features based on Prophet feature extraction combined with the gating and attention mechanism. The structure also has the characteristics of a strong anti-noise ability of the LightGBM method, which can reduce the impact of the energy consumption mutation point on the overall prediction effect of the model. In addition, we propose a simple method to select the hyperparameters of the time window length using ACF and PACF diagrams. The MAPE of the GRU-MMattention-LightGBM model is 1.69%, and the relative error is 8.66% less than that of the GRU structure and 2.02% less than that of the LightGBM prediction. Compared with a single method, the prediction accuracy and stability of this hybrid architecture are significantly improved.