A model predictive control optimization environment for real-time commercial building application

Corbin, Charles D.; Henze, Gregor P.; May-Ostendorp, Peter

doi:10.1080/19401493.2011.648343

Cited by 148 publications

(87 citation statements)

References 4 publications

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“…As shown in Figure 8, there was agreement among most interviewees (22) to set thermal comfort as the main constraint followed by cost (18).Interviewees refer to comfort conditions defined by standards. There was an agreement to consider constrains as primarily to define the feasible domain.…”

Section: B) What Kind Of Constraints Do You Set For Optimisation?mentioning

confidence: 99%

“…Although optimisation studies are most commonly performed in the early-design stage, where the majority of design decisions have yet to be made, optimisation approaches can be equally useful in the late-design stages for selecting and fine-tuning control strategies and HVAC design and during building operations to best select building control based on model-predictive control strategies [21][22][23][24]. The most appropriate search algorithms and modelling approaches vary depending on the application area, but the suitable application area for optimisation methodologies related to building design and control is vast and constantly evolving.…”

Section: Importance Of Bpomentioning

confidence: 99%

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Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

Attia

Hamdy

O’Brien³

et al. 2013

Energy and Buildings

349

192

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This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimisation tools in Net Zero Energy Buildings (NZEB) Design. The paper reviews current trends in simulationbased Building Performance Optimisation (BPO) and outlines major criteria for optimisation tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimisation experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB Design. The findings indicate existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.

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Section: B) What Kind Of Constraints Do You Set For Optimisation?mentioning

confidence: 99%

Section: Importance Of Bpomentioning

confidence: 99%

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

Attia

Hamdy

O’Brien³

et al. 2013

Energy and Buildings

349

192

View full text Add to dashboard Cite

show abstract

“…In addition, proprietary software solutions based on Java enable the connection and integration, thus interoperability, of control devices communicating in BACnet, ModBus, LonTalk, or OPC utilizing Java Application Control Engine (JACE) controllers to gain insight and active control of several geographically dispersed buildings and potential integration with a smart grid aggregation entity. As an illustration, it is technically possible, and practiced by a buildingto-grid startup company [31], to compute optimal global temperature setpoints that minimize expected operating cost or achieve desired demand reduction targets on a cloud platform and communicate these with the building in real time using such an interoperability approach. New technology introduced recently can allow for legacy pneumatic thermostats to be wirelessly connected to building control systems, thus giving control functionality similar to modern direct digital control.…”

Section: What Can or Should Be Done To Legacy Hvac Control Systems Tomentioning

confidence: 99%

Ten questions concerning integrating smart buildings into the smart grid

Lawrence

Boudreau

Helsen

et al. 2016

Building and Environment

116

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Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be 'prosumers' on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and nontechnical areas.

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“…Buildings account for 40% of all electricity consumed in the United States, with half that figure attributed to heating, ventilation and air conditioning (HVAC) [11,12]. There is an extensive literature on adjusting the timing of air conditioner operation in commercial buildings to reduce demand during high-cost times and increase it in low-cost times [13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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

Izawa¹,

Fripp²

2018

Preprint

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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, occupancy and lighting. 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, non-linear 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 3% reduction in costs vs. a "brick-wall" MPC approach with similar comfort and 13% reduction in costs vs. a standard night setback strategy. CCPSO also reduced peak-hours demand by 3% vs. the "brick-wall" strategy and 15% vs. standard night-setback. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% vs. the "brick-wall" strategy. 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. Keywords: HVAC model predictive control, demand response, EnergyPlus, particle swarm optimization (PSO), renewable energy, smart grids MSC: 49M37, 65K05, 90-04, 90B35, 90B50, 90C29, 90C56, 90C90, 91B08, 91B10, 91B26, 91B42, 91B74

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A model predictive control optimization environment for real-time commercial building application

Cited by 148 publications

References 4 publications

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

Ten questions concerning integrating smart buildings into the smart grid

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

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