@conference {1504000,
	title = {Predicting HVAC Energy Consumption in Commercial Buildings using Multiagent Systems },
	booktitle = {International Symposium on Automation and Robotics in Construction (ISARC)},
	year = {2013},
	abstract = {Energy consumption in commercial buildings has been increasing rapidly in the past decade. The
knowledge of future energy consumption can bring significant value to commercial building energy
management. For example, prediction of energy consumption decomposition helps analyze the energy
consumption patterns and efficiencies as well as waste, and identify the prime targets for energy
conservation. Moreover, prediction of temporal energy consumption enables building managers to plan out
the energy usage over time, shift energy usage to off-peak periods, and make more effective energy
purchase plans. This paper proposes a novel model for predicting heating, ventilation and air conditioning
(HVAC) energy consumption in commercial buildings. The model simulates energy behaviors of HVAC
systems in commercial buildings, and interacts with a multiagent systems (MAS) based framework for
energy consumption prediction. Prediction is done on a daily, weekly and monthly basis. Ground truth
energy consumption data is collected from a test bed office building over 267 consecutive days, and is
compared to predicted energy consumption for the same period. Results show that the prediction can match
92.6 to 98.2\% of total HVAC energy consumption with coefficient of variation of the root mean square
error (CV-RMSE) values of 7.8 to 22.2\%. Ventilation energy consumption can be predicted at high
accuracies (over 99\%) and low variations (CV-RMSE values of 3.1 to 16.3\%), while cooling energy
consumption accounts for majority of inaccuracies and variations in total energy consumption prediction.},
	author = {Li, Nan and Kwak, Jun-young and Becerik-Gerber, Burcin and Tambe, Milind}
}
