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Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has...
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Context 1
... thermal envelope zone model is shown in Fig. 4. The basic model is an R2C2 envelope model, with additional components specific to the field test building, such as air-based HVAC supply and return and reheat heat exchanger. The solar irradiation is split so that a fraction is assumed to add energy to the exterior wall and the rest is assumed to add energy to the inside thermal mass. ...
Context 2
... energy to the inside thermal mass. The following parameters are estimated using measured data by the parameter estimation algorithm described in Section 4.4: í µí± í µí±¤1, í µí± í µí±¤2, í µí° ¶í µí±, í µí° ¶í µí±¤, í µí°¹ í µí± í µí±í µí± í µí± , í µí°¹ í µí± í µí±í µí± í µí±¤ , í µí°¹ í µí±í µí±í µí±¡ (see the schematic model view in Fig. 4 for how these parameters are used). In early parameter estimation trials, both R2C2 and R3C3 models were tested, with the R2C2 model performing sufficiently without the additional dimensions of the estimated parameter space added by the R3C3 ...
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Citations
... Despite the importance of field demonstrations in advancing real-world adoption, the research community currently lacks a systematic and comprehensive review of field demonstrations. Sturzenegger et al. [14] and Blum et al. [15] proposed and tested their own MPC systems in real-world commercial buildings while reviewing a limited number of publications on field demonstrations of MPC in commercial buildings, covering 10 and 17 studies, respectively. Similarly, Pergantis et al. [16] conducted a partial review of field demonstrations of MPC and RL in residential buildings, analyzing 12 papers, and deployed their own MPC system in a real house. ...
... In addition to their review, they conducted a seven-month MPC experiment in a Swiss office building, showing that MPC provides significant energy savings and comfort improvements, but also requires high implementation costs. Blum et al. [15] investigated 14 field demonstration papers on MPC in commercial buildings. They detailed the key aspects of each paper, such as building type, system type, control variables and objectives, MPC approaches, test periods, and results. ...
... Most studies that controlled a small subset of building spaces are filtered out due to the risk that they overestimate savings. However, we include papers [85,86,87,88,89,15,90] that controlled large subsets of commercial building spaces, leaving out only a few small zones due to restrictions imposed by building managers. Many of these studies also report good insulation between zones, suggesting negligible heat transfer with adjacent zones. ...
A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. This paper reviews 24 field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information -- such as experiment scopes, locations, and durations -- this paper analyzes experiment protocols and their influence on performance estimates. We find that 71% of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29% that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16% in residential buildings and 13% in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.
... Over two months, the data demonstrated a 40% decrease in HVAC energy use. Nevertheless, significant work was needed for activities like data collecting and controller deployment, indicating areas where future MPC implementations may be made more efficient (Blum et al., 2022). Jordi Maci� a Cid conducted another study that develops a MPC optimization algorithm for HVAC systems in multifamily buildings, using Mixed Integer-Linear Programming (MILP) and the OR-Tools library. ...
Purpose
The major goals of this study are to investigate the existing state of affairs concerning daylighting, glare and thermal comfort in the building, which will be the focus of the investigation, particularly in connection to the building’s energy efficiency considering student task performances. EnergyPlus, which is a software for building energy modeling, is applied to evaluate the energy consumption of buildings under a variety of circumstances related to their design. With the use of a variety of simulations, the goal of this study is to provide building design options that are both the most efficient and the most cost-effective for the case study building.
Design/methodology/approach
There are three distinct categories that can be applied throughout the entire process. The first thing that has to be done is to choose a building to use as a case study and then conduct an analysis of the current situation to address the existing problem. In the following step, several passive design measures are implemented and simulated in order to solve the challenges that are currently being faced. A detailed cost analysis is carried out as the final step in the process of determining whether or not it is feasible.
Findings
According to the findings: A cantilevered 3’ drop wall as a shading system, an east-facing model, and a window with a height of 4.5 is the ideal configuration. The annual energy can be saved by 22.5%. The payback period is 11 years. Benefit/Cost >1 makes it a feasible and economical solution.
Originality/value
The work is completely original. No one have done this before.
... • For Berkeley, the weather forecasts were obtained from the DarkSky API (Apple Inc) and stored for a field demonstration of MPC (Blum et al. 2022). During the same demonstration, the measured data were collected from a weather station on LBNL's campus and accessed through (The University of Utah). ...
... Therefore, the GHI forecasts are generated using the k-Nearest Neighbors (kNN) machine learning algorithm, trained using 30 days of recent past measurements of GHI from the weather station in combination with past 1-step cloud cover, ambient temperature forecasts and predicted clear-sky irradiance. More details can be found in (Blum et al. 2022 • For Leuven, the weather forecasts were purchased from the OpenWeather service (Open-Weather). The measured data were collected at thpdme Vliet Building in Leuven, operated by the Building Physics Section of the KU Leuven. ...
... Although no experimental studies have considered a time-varying indoor humidity in residential buildings, in commercial buildings, the work in [ 50 ] found only 2 out of a total of 13 studies that did [ 71 ], [ 72 ]. The study in [ 71 ], developed a MPC for active chilled beams. ...
... In so far as the author is aware and as per the literature review of [ 32 ] and [ 50 ], three experiments of supervisory control of HVAC equipment on the residential side have looked at reducing the peak demand [ 10 ], [ 32 ], [ 33 ], [ 87 ], while on the commercial there have been five. ...
... Pergantis et al. [ 32 ], as well as Blum et al. [ 50 ], discuss the practical challenges of supervisory HVAC control. This study represents the second time that a supervisory HVAC control system was demonstrated at the DC Nanogrid House. ...
Supervisory predictive control of residential building heating, ventilation, and air conditioning (HVAC) systems could protect electrical infrastructure, enhance occupants’ thermal comfort, reduce energy costs, and minimize emissions. However, there are few experimental demonstrations, with most of the work focusing on simulation studies. To convince stakeholders of the benefits of supervisory predictive controls for residential HVAC systems, it is important to demonstrate practical systems in real buildings. Practical demonstrations also further our understanding of the field performance of these systems. This thesis presents the first comprehensive review of supervisory predictive control experiments in residential buildings, drawing critical insights on the estimated energy savings, the types of equipment controlled, the objectives and problem formulations considered, and other practical considerations. To address limitations in the existing body of experimental work, a series of field demonstrations were performed in a real house with student occupants near the Purdue campus in West Lafayette, Indiana, U.S.A.
The first field demonstration involved supervisory predictive control of an air-to-air heat pump with backup electric resistance heat. This was the first experiment to consider this equipment configuration, which is common in North America. A simple data-driven method is presented for learning a model of the temperature dynamics of a detached residential building. Using this model, the control system adjusts indoor temperature set points based on weather forecasts, occupancy conditions, and data-driven models of the heating equipment. Field tests from January to March of 2023 included outdoor temperatures as low as −15 ℃. During these tests, the control system reduced total heating energy costs by 19% on average (95% confidence interval: 13–24%) and energy used for backup heat by 38%. The control system also reduced the frequency of using high-stage (19 kW) backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.
The second field demonstration advanced the state of the art of predictive residential cooling control, wherein past experimental demonstrations relied on “sensible” models of building thermal dynamics and neglected humidity effects. In this thesis, a model-free machine learning method is introduced to predict the indoor wet-bulb temperature and the sensible heat ratio in a “latent” model formulation, with the aim to increase the accuracy of the real electrical power prediction. The latent and sensible formulations are tested in two separate model predictive controller (MPC) schemes in an on-off fashion. One MPCscheme aims to reduce energy costs while enhancing comfort. The other is a power-limiting controller that aims to keep the power of the HVAC equipment below 2.5 kW between 4 PM and 8 PM. The two MPC schemes and the two load models are assessed through 38 days of testing. It is found that across both economic MPC and power-limiting MPC, the energy savings across the latent and sensible formulations are similar. Through a normalized Cooling Degrees Days analysis, the energy savings to the baseline controller in the house are found to be 16 to 32% for economic MPC (95% confidence interval) and -5 to 10% for power-limiting MPC, with 7 to 21% savings across both controllers (14% mean). For power limiting, the latent formulation reduced the total duration of constraint violation by 88% and the sensible formulation by 40%, with respect to the non-MPC baseline. Additionally, the latent formulation reduced the peak power demand by 13% relative to the baseline, a behavior not observed in the sensible formulation.
The third field experiment investigated the problem of protecting home electrical infrastructure in the context of electrification retrofits. Installing electric appliances or vehicle charging in a residential building can sharply increase the electric current draws. In older housing, high current draws can jeopardize circuit breaker panels or electrical service (the wires that connect a building to the distribution grid). Upgrading electrical panels or service often entails long delays and high costs, and thus it poses a significant barrier to electrification. This thesis develops and field tests a novel control system that avoids the need for electrical upgrades by maintaining an electrified home’s total current draw within the safe limits of its existing panel and service. In the proposed control architecture, a high-level controller plans device set-points over a rolling prediction horizon, while a low-level controller monitors real-time conditions and ramps down devices if necessary. The control system was tested for 31 consecutive winter days with outdoor temperatures as low as -20 ℃. The control system maintained the whole-home current within the safe limits of electrical panels and service rated at 100 A, a common rating for older houses in North America, by adjusting only the temperature set-points of the heat pump and water heater. Simulations suggest that the same 100 A limit could accommodate a second electric vehicle (EV) with Level II (11.5 kW) charging. The proposed control system could allow older homes to safely electrify without upgrading electrical panels or service, saving a typical household on the order of 10,000.
These three field experiments demonstrate that low-cost predictive control systems can serve multiple objectives, improving the efficiency of heat pumps and water heaters while maintaining comfort and protecting electrical infrastructure. Future work will be directed toward improving the scalability of these proposed controllers through the incorporation of data-driven methodologies such as data-enabled predictive control, as well as understanding the application of these algorithms with different systems, including batteries, on-site solar photovoltaics, and electrical vehicle charging.
... Since MPC solves optimization problems based on system dynamics, the model must accurately capture the system's response to control inputs and disturbances. However, developing and maintaining such a model requires detailed building information, expert knowledge, substantial modeling efforts, and customized case-by-case calibrations [8,9]. This challenge potentially hinders its large-scale commercial applications. ...
Model predictive control can achieve significant energy savings, offer grid flexibility, and mitigate carbon emissions. However, the challenge of identifying individual control-oriented building dynamic models limits large-scale real-world applications. To address this issue, this study proposed a Modularized Neural Network Incorporating Physical Priors (ModNN), capable of establishing a control-oriented and physical-consistent building dynamic model within minutes without substantial modeling effort. This is also the first study to evaluate the physical consistency of a given data-driven model both qualitatively and quantitively. We compared the physical consistency of a classical Long Short-Term Memory (LSTM) model and our ModNN. The ModNN strictly satisfies physical constraints, whereas the LSTM model learned contradictory system dynamics. Additionally, we compared their control performance on an EnergyPlus virtual testbed. While the LSTM model demonstrated slightly better prediction accuracy in dynamic modeling, it failed in control optimization, resulting in an 89{\deg}C-h temperature violation, whereas the ModNN showed only a 0.57{\deg}C-h violation and achieved up to a 78% peak load reduction. Our findings highlight the importance of incorporating physics priors into data-driven models and provide a promising solution for future smart building control optimization. Furthermore, the proposed evaluation framework defines two physical consistency indicators, providing guidelines for selecting and testing control-oriented, data-driven building dynamic models.
... The integration of semantic technologies allows for the transformation of traditional structured data into rich knowledge graphs that capture both the physical relationships and operational dynamics of building systems. For example, recent work has demonstrated how ontology-based semantic models can automatically generate and calibrate building energy models while maintaining a high accuracy across multiple measuring devices [53]. Knowledge graphs particularly excel in representing highly interconnected building data, as they can model complex relationships between components, sensors, and control systems while supporting reasoning mechanisms and logic-based methods [37]. ...
This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet the increasing energy demands and the new requirements of renewable source integration, DTs offer promising solutions to enhance the monitoring, control, and optimization of these systems. In this paper, we examine the concept of DTs in the context of smart grids, and their requirements, challenges, and integration with the Internet of Things (IoT) and Artificial Intelligence (AI). We also discuss different applications in asset management, system operation, and disaster response. This paper analyzes current challenges, including data management, interoperability, cost, and ethical considerations. Through case studies from various sectors in Canada, we illustrate the real-world implementation and impact of DTs. Finally, we discuss emerging trends and future directions, highlighting the potential of DTs to revolutionize smart grid networks and contribute to more efficient, reliable, and sustainable power systems.
... For example, Sturzenegger et al. found after deploying MPC in five floors of an office building that "the required initial investment is likely too high to justify the deployment in everyday building projects on the basis of operating cost savings alone" [5]. Similarly, Blum et al. reported that implementing MPC in two floors of an office building required 10 person-months of engineering labor [8]. ...
... David Blum from Lawrence Berkeley National Laboratory (LBNL) presented field demonstrations of the open-source MPCPy platform to reduce energy use by rooftop units (RTUs) that condition an office building on LBNL's campus [8,20]. Blum's team modeled each zone as a second-order thermal circuit, used polynomial curve fits to represent fan and compressor powers, and adjusted RTU supply air temperature and fan speed set-points. ...
... David Blum, a research scientist at Lawrence Berkeley National Laboratory (LBNL), presented on field demonstrations of MPC in two stories of an office building on LBNL's campus, covering 60,000 ft 2 of conditioned floor area [8,20]. A separate data center in the same building was not part of the control study. ...
Over the last two decades, research and development efforts have shown that advanced control of heating, ventilation, and air conditioning (HVAC) equipment in commercial buildings can improve energy efficiency, reduce emissions, and turn buildings into active participants in the power grid. Despite these efforts, advanced commercial HVAC control has not yet seen widespread adoption. In this paper, we argue that the research community can help companies deploy advanced HVAC control at speed and scale by reorienting research efforts toward clearly demonstrating the business case for adoption. To support this argument, we draw on findings from the 2023 Intelligent Building Operations Workshop, which brought together researchers, entrepreneurs, and representatives from industry and government to discuss current business offerings, state-of-the-art field demonstrations, barriers to adoption, and future directions.
... For instance, Yang et al. [22] employed a machine learning-based MPC framework to control indoor temperatures in underground offices not exposed to outdoor environments, achieving a 26% energy consumption reduction while ensuring thermal comfort. Blum et al. [23] developed an MPC controller using the free and open-source toolchain MPCPy to help reduce implementation costs associated with controller software development. Xiao et al. [24] proposed a modeling approach based on physically consistent deep learning (PCDL) to optimize energy efficiency and indoor thermal comfort for heating in five regional areas. ...
... Actual photo of the experimental platformAccording to the experimental design and hydraulic calculations, the three-view diagram of the physical experimental platform is shown inFig. 7. Hot water is heated by heating rods in the constant temperature water tank (①), pumped upward by the circulation pump (②) to the main supply line, passes through each branch for heat23 dissipation, then flows downward to the main return line, and finally returns to the constant temperature water tank. Each branch, from top to bottom, consists of: manual valve (③), supply water temperature sensor (④), supply branch pipe (⑤), electric ball valve (⑥), water-cooled radiator with fan (⑦), rotor flowmeter (⑧), return water temperature sensor (⑨), and return branch pipe (⑩). ...
... The MPC has been used in building energy control for a variety of objectives, including indoor comfort [21] and energy cost reduction [22], thanks to advancements in computer technology. During a two-month test period, Blum et al. achieved roughly 40% energy savings on an HVAC system in a real office building in the United States by testing the influence of the MPC algorithm [23]. Freund et al. showed the Model Predictive Control (MPC) algorithm's potential for energy savings by successfully implementing it in a sizable building. ...
In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.
... However, grey-box models are currently not as popular for DT applications, as modeling expertise and system topology information is required to define the model structure, making them time-consuming to build and maintain in a DT context. Improving interoperability between data models and simulation models is therefore crucial to overcome these challenges and unlock proven model-based services that can greatly improve building operation efficiency [9,29,30]. ...
... An example of a component model related to the case study and its accompanying signature pattern is shown in Figure 4 and Figure 5. Here, Figure 4 shows a Modelica diagram representing the heat and mass dynamics of a space. The model is largely based on the grey-box model developed by Blum et al. [30]. It includes an outdoor boundary, one adjacent space, a space heater, and balanced supply and exhaust ventilation. ...
... For each scenario, the simulation model is automatically generated based on the data in the semantic model. To evaluate the scenarios, the accumulated deviation from the heating setpoint, given by Equation 1, is used as a comparison metric [30,44,45]. The metric captures both the intensity and duration of setpoint violations and has the unit Kelvin-hours. ...
Digital twins have emerged as a promising concept for improving building energy efficiency, but their implementation faces challenges in interoperability and adaptability. This paper presents a large-scale field demonstration of an interoperable energy modeling framework for building digital twins, using ontology-based semantic models as data sources for automated model generation and calibration of data-driven component models. The study focuses on a single floor of a hospital building, comprising 12 conditioned zones and data from 45 measuring devices. Across the 45 sensors, the model achieved on average mean absolute errors of 0.40°C for temperature, 32 ppm for CO2 concentration, 0.06 for valve position, and 0.04 for damper position predictions. These results demonstrate the framework's ability to generate and calibrate accurate and flexible building energy models with reduced effort. The paper also showcases the framework's practical application in exploring system modifications to improve indoor comfort, highlighting its potential for scenario analysis and decision support. The proposed approach significantly streamlines the process of creating and maintaining accurate, up-to-date energy models, offering a robust foundation for digital twin applications in the built environment.
