ew 18: e16

Research Article

Extreme Gradient Boosting Algorithm for Energy Optimization in buildings pertaining to HVAC plants

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  • @ARTICLE{10.4108/eai.13-7-2018.164562,
        author={Monika Goyal and Mrinal Pandey},
        title={Extreme Gradient Boosting Algorithm for Energy Optimization in buildings pertaining to HVAC plants},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={5},
        keywords={Machine Learning, Ensemble, Energy Optimization, HVAC, Extreme Gradient Boostin},
        doi={10.4108/eai.13-7-2018.164562}
    }
    
  • Monika Goyal
    Mrinal Pandey
    Year: 2020
    Extreme Gradient Boosting Algorithm for Energy Optimization in buildings pertaining to HVAC plants
    EW
    EAI
    DOI: 10.4108/eai.13-7-2018.164562
Monika Goyal1, Mrinal Pandey1,*
  • 1: Department of Computer Science and Technology, ManavRachna University, Faridabad, India
*Contact email: mrinal@mru.edu.in

Abstract

With the recent advancements in technology, energy is being consumed at a great pace in almost every region. Buildings are the biggest consumer of energy, almost 40% of total energy is being consumed by the buildings. The purpose of this research is to investigate Ensemble Learning based optimal solution for predicting energy consumption in Heating, Ventilation and Air Conditioning (HVAC) plants as the HVAC unit consumes a large percentage of energy in buildings. The study focuses on Cooling Tower data of HVAC plants as Cooling Tower carries a major responsibility for maintaining ambient within a building. In this paper, four Regression techniques namely Multiple Linear Regression, Random Forests, Gradient Boosting Machines and Extreme Gradient Boosting have been experimented. The findings reveal that Extreme Gradient Boosting Ensemble outperforms with higher accuracy and lower in overfitting.