Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower

Hongfang Lu, Feifei Cheng, Xin Ma and Gang Hu

Energy, 2020, vol. 203, issue C

Abstract: Accurate prediction of building energy consumption is crucial for building energy management. However, the building energy consumption is affected by many factors and shows obvious nonlinear characteristics in the time series, which is difficult to predict. In this work, a novel hybrid model is proposed for predicting short-term building energy consumption. In this model, the raw data is decomposed into multiple smooth datasets using complete ensemble empirical mode decomposition with adaptive noise, and the building energy consumption is predicted by the traditional extreme gradient boosting. Taking the daily energy consumption of the City of Bloomington Intake Tower as the simulation object, the results show that the mean absolute percentage error of the proposed model is 4.85%, which is much lower than that of five benchmark models. The proposed model is also applied to the prediction of other parameters related to the energy consumption of the intake tower, and shows good prediction performance. Moreover, the influences of the sliding window length and data attributes on the prediction results are also discussed.

Keywords: Building energy consumption; Intake tower; Peak power; Machine learning; Extreme gradient boosting (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:203:y:2020:i:c:s036054422030863x

DOI: 10.1016/j.energy.2020.117756

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