Abstract
Today energy optimization has become a great challenge as energy is being consumed at a fast rate in almost every sector including buildings, transport and industries. However, Buildings are the largest consumer of energy followed by Transport and Industry throughout the world. In buildings, most of the energy consumption depends upon the usages of air conditioning plants (Heating, Ventilation and Air Conditioning). Therefore, with the necessity to determine the energy consumption due to HVAC plant in building, this research focuses on Cooling Tower data of HVAC plant of a building as Cooling Tower is an important component of HVAC and carries a major responsibility of maintaining the ambient temperature within a building. In this paper, three popular Machine Learning techniques namely Multiple Linear Regression, Random Forests and Gradient Boosting Machines were experimented for predicting the energy consumption due to HVAC plant within a building. The findings of the experiments reveal that Random Forest outperforms in terms of error measures.
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Goyal, M., Pandey, M. (2020). Towards Prediction of Energy Consumption of HVAC Plants Using Machine Learning. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_22
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DOI: https://doi.org/10.1007/978-981-15-5827-6_22
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