Time Series Analysis of Household Electric Consumption with XGBoost Model

Bezzar Nour El Houda, Laimeche Lakhdar, A. Meraoumia
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Abstract

Due to the improvement of population quality of life over the world and the following increase of energy demand in particularly the electricity, it has become necessary to follow the evolution of its consumption. Electricity consumption forecasting is considered as key factor in a process of improving energy efficiency, controlling consumption and reducing costs. The main objective of this paper consist to propose a forecast model for household electricity consumption using XGBoost regressor applied on a dataset which contains data collected from a house situated in Sceaux (Paris, France) between December 2006 and November 2010. The experimental results show that the proposed model achieved a higher performance for forecasting periods, particularly, in hourly and daily granularities in terms of RMSE and MAEP.
基于XGBoost模型的家庭用电量时间序列分析
由于世界各地人口生活质量的提高和随之而来的能源需求的增加,特别是电力需求的增加,有必要跟踪其消费的演变。电力消费预测是提高能源效率、控制消费和降低成本的关键因素。本文的主要目标是提出一个家庭用电量的预测模型,该模型使用XGBoost回归量应用于一个数据集,该数据集包含2006年12月至2010年11月期间从法国巴黎的一所房子收集的数据。实验结果表明,该模型在预测时段,特别是在小时和日粒度的RMSE和MAEP方面取得了较高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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