Short Term and Long term Building Electricity Consumption Prediction Using Extreme Gradient Boosting

Abstract

Background: Electricity is considered as the essential unit in today’s high-tech world. The electricity demand has been increased very rapidly due to increased urbanization,(smart buildings, and usage of smart devices to a large extent). Building a reliable and accurate electricity consumption prediction model becomes necessary with the increase in demand for energy. From recent studies, prediction models such as support vector regression (SVR), gradient boosting decision tree (GBDT), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost) have been compared for the prediction of electricity consumption and XGBoost is found to be the most efficient method that leads to the motivation for the research. Objective: The objective of this research is to propose a model that performs future electricity consumption prediction for different time horizons: short term prediction and long term prediction using the extreme gradient boosting method and reduce prediction errors. Also, based on the prediction of the electricity consumption, the best and worst predicted days are being recognized. Methods: The method used in this research is the extreme gradient boosting for future building electricity consumption prediction. The extreme gradient boosting method performs predictions for different time horizons(short term and long term) for different seasons(summer and winter). The model was designed for a house building located in Paris. Results: The model has been trained and tested on the dataset and its prediction is accurate with the low rate of errors compared to other machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28, which is the least value for errors when compared to the baseline prediction models. Conclusion: A model that is robust to all the conditions should be built by enhancing the prediction mechanism such that the model should be dependent on a few factors to make electricity consumption prediction.