Robert Turney, Ph.D.
Associate Professor Milwaukee School of Engineering
- Milwaukee WI
Dr. Robert Turney is an expert in the areas of digital image and video processing, real-time embedded systems and products.
Milwaukee School of Engineering
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Education, Licensure and Certification
B.S.
Electrical Engineering
University of Wisconsin-Milwaukee
1989
Ph.D.
Electrical Engineering
University of Wisconsin-Milwaukee
2005
M.S.
Electrical Engineering
University of Wisconsin-Milwaukee
1992
Biography
Areas of Expertise
Accomplishments
Johnson Controls Merit Award
2018
Model Based Design for VRF Systems
Inventor of the year
2017
Johnson Controls
Johnson Controls Chairman’s Award
2016
Stanford Energy Facility, EOS optimization system
Best of the Best
2016
Engineering News Record, Stanford Central Energy facility
Affiliations
- Institute of Electrical and Electronics Engineers (IEEE) : Senior Member
- UWM Industrial Liaison Board : Member
Social
Patents
Systems and methods for rapid disturbance detection and response
US9568204B2
2017
A method for detecting and responding to disturbances in a HVAC system using a noisy measurement signal and a signal filter is provided. The method includes detecting a deviation in the noisy measurement signal, resetting the filter in response to a detected deviation exceeding a noise threshold, filtering the noisy measurement signal using the signal filter to determine an estimated state value, and determining that a disturbance has occurred in response to the estimated state value crossing a disturbance threshold.
Systems and methods for cascaded model predictive control
US9852481B1
2017
Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral.
Systems and methods for energy cost optimization in a building system
US9436179B1
2016
Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral.
System identification and model development
US9235657B1
2016
Methods for system identification are presented using model predictive control to frame a gray-box parameterized state space model. System parameters are identified using an optimization procedure to minimize a first error cost function within a range of filtered training data. Disturbances are accounted for using an implicit integrator within the system model, as well as a parameterized Kalman gain.
Low level central plant optimization
US10101731B2
2015
Systems and methods for low level central plant optimization are provided. A controller for the central plant uses binary optimization to determine one or more feasible on/off configurations for equipment of the central plant that satisfy operating constraints and meet a thermal energy load setpoint.
Selected Publications
Cost optimization of combined building heating/cooling equipment via mixed-integer linear programming
IEEE2015
In this paper, we propose a mixed-integer linear program to economically optimize equipment usage in a central heating/cooling plant subject to time-of-use and demand charges for utilities. The optimization makes both discrete on/off and continuous load decisions for equipment while determining utilization of thermal energy storage systems.
System Identification for Model Predictive Control of Building Region Temperature
International High Performance Buildings Conference2016
Model predictive control (MPC) is a promising technology for energy cost optimization of buildings because it provides a natural framework for optimally controlling such systems by computing control actions that minimize the energy cost while meeting constraints.
Closed-Loop Scheduling for Cost Minimization in HVAC Central Plants
International High Performance Buildings Conference2016
In this paper, we examine closed-loop operation of an HVAC central plant to demonstrate that closed-loop receding-horizon scheduling provides robustness to inaccurate forecasts, and that economic performance is not seriously impaired by shortened prediction horizons or inaccurate forecasts when feedback is employed.
Distributed economic model predictive control for large-scale building temperature regulation
IEEE2016
Although recent research has suggested model predictive control as a promising solution for minimizing energy costs of commercial buildings, advanced control systems have not been widely deployed in practice. Large-scale implementations, including industrial complexes and university campuses, may contain thousands of air handler regions each with tens of zones.
A mixed-integer linear programming model for real-time cost optimization of building heating, ventilation, and air conditioning equipment
Energy and Buildings2017
In this paper, we present a framework for the formulation and solution of mixed-integer linear programming (MILP) models for operational planning of HVAC systems in commercial buildings. We introduce the general concepts of generators (e.g., chillers, boilers, cooling towers) and resources (e.g., electricity, chilled water), which allow us to model a wide range of central plants.
