{'fraction_non_alphanumeric': 0.017111222949169603, 'fraction_numerical': 0.0035228988424760945, 'mean_word_length': 5.079510703363915, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Short term and Long term Building Electricity Consumption Prediction Using Extreme Gradient Boosting



 Electricity is considered as the basic 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 building 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 most efficient
thus leading to the motivation for the proposed research work.



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 extreme gradient boosting method and
reduce the prediction errors. In addition to this based on the prediction, best and worst predicted days are also recognized.



The method used in this research is the extreme gradient boosting for future building electricity consumption
prediction. The extreme gradient boosting method performs prediction for the short term and long term for different
seasons. The model is trained on a household building in Paris.



The model is trained and tested on the dataset and it predicts accurately with the lowest errors compared to other
machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28 which is the least
errors when compared to the baseline prediction models.



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 less factors to make electricity consumption prediction.
