Journal article
Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method
Energy conversion and management, Vol.85, pp.146-153
09/2014
DOI: 10.1016/j.enconman.2014.05.053
Abstract
•We study the energy saving of HVAC systems with a data-driven approach.•We conduct an in-depth analysis of the topology of developed Neural Network based HVAC model.•We apply interior-point method to solving a Neural Network based HVAC optimization model.•The uncertain building occupancy is incorporated in the minimization of HVAC energy consumption.•A significant potential of saving HVAC energy is discovered.
In this paper, a data-driven approach is applied to minimize energy consumption of a heating, ventilating, and air conditioning (HVAC) system while maintaining the thermal comfort of a building with uncertain occupancy level. The uncertainty of arrival and departure rate of occupants is modeled by the Poisson and uniform distributions, respectively. The internal heating gain is calculated from the stochastic process of the building occupancy. Based on the observed and simulated data, a multilayer perceptron algorithm is employed to model and simulate the HVAC system. The data-driven models accurately predict future performance of the HVAC system based on the control settings and the observed historical information. An optimization model is formulated and solved with the interior-point method. The optimization results are compared with the results produced by the simulation models.
Details
- Title: Subtitle
- Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method
- Creators
- Andrew Kusiak - University of IowaGuanglin Xu - University of IowaZijun Zhang - University of Hong Kong
- Resource Type
- Journal article
- Publication Details
- Energy conversion and management, Vol.85, pp.146-153
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.enconman.2014.05.053
- ISSN
- 0196-8904
- eISSN
- 1879-2227
- Language
- English
- Date published
- 09/2014
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984186686702771
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