Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

Abstract

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

Keywords

• Energy management
• Deep learning
• Autoencoders
• Decision trees
• Meteorological data
• Microgrid optimization