Next Article in Journal
Dimensional Analysis of Power Prediction of a Real-Scale Wind Turbine Based on Wind-Tunnel Torque Measurement of Small-Scaled Models
Next Article in Special Issue
Application of Wind as a Renewable Energy Source for Passive Cooling through Windcatchers Integrated with Wing Walls
Previous Article in Journal
On Small Signal Frequency Stability under Virtual Inertia and the Role of PLLs
Previous Article in Special Issue
Theoretical and Experimental Contributions on the Use of Smart Composite Materials in the Construction of Civil Buildings with Low Energy Consumption
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Energies 2018, 11(9), 2373; https://doi.org/10.3390/en11092373

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

Department of Electrical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
*
Author to whom correspondence should be addressed.
Received: 6 August 2018 / Revised: 6 September 2018 / Accepted: 6 September 2018 / Published: 8 September 2018
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
Full-Text   |   PDF [845 KB, uploaded 13 September 2018]   |  

Abstract

A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a “brick wall” preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar “brick-wall” MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the “brick-wall” approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the “brick-wall” strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. View Full-Text
Keywords: heating ventilation and air conditioning (HVAC); model predictive control (MPC); demand response; EnergyPlus; particle swarm optimization (PSO); renewable energy; smart grids heating ventilation and air conditioning (HVAC); model predictive control (MPC); demand response; EnergyPlus; particle swarm optimization (PSO); renewable energy; smart grids
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Izawa, A.; Fripp, M. Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance. Energies 2018, 11, 2373.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top