Model-based and model-free “plug-and-play” building energy efficient control
Introduction
Several studies, both from the US Department of Energy and from the EU Directorate of Energy, have demonstrated the potentialities of Building Optimization and Control (BOC) systems to provide improved energy efficiency in commercial and residential buildings [1], [2]. A BOC system, also referred to as energy management system, is a system of computer-aided tools aiming at monitoring and controlling electrical building loads, mainly heating, ventilation and air conditioning (HVAC) units [3]. Deploying BOC systems is becoming more and more affordable and easy: however, their practical use is still very limited and their energy efficiency potentialities are not fully exploited: currently, the vast majority of BOC-equipped buildings adopts simple rule-based BOC logics with limited energy efficiency capabilities [4], [5]. The main reason for the limited use of more advanced BOC systems that can potentially provide more significant energy efficiency than rule-based solutions is the fact that the design and deployment of advanced BOC systems requires a tedious and time-consuming manual calibration procedure. The need for a manual calibration procedure comes from the fact that most advanced BOC design approaches are based on simplified (typically linear) models for the building dynamics, as the use of more complex and realistic models renders the BOC design practically not possible in most situations. This is the case, for example, of model predictive control-based BOC systems, where the use of linear models makes the optimization task feasibly implementable, while the use of nonlinear building models makes it computationally cumbersome [6], [7], [8], [9], [10], [11]. As a result of using simplified models, the derived advanced BOC systems, when applied in the real-world buildings, might exhibit suboptimal performance which, sometimes, can be poor or even unacceptable [12], [13]: qualified personnel is then extensively engaged in a period of calibration activity that is required to elevate the advanced BOC system to its best achievable performance. Such calibration procedure can be either assisted by Building Energy Performance Simulation (BEPS) models providing elaborate and accurate descriptions of the building dynamics [14], [15], [16], or directly performed on the real building by tuning the BOC system during its implementation. In both cases, the fine-tuning procedure is time-consuming, conducted manually, and mainly relying on expertise and human judgment.
Recently, there has been a considerable research effort toward the development of “plug-and-play” BOC designs which have the potential of overcoming the need for a time-consuming manual fine-tuning procedure. Such “plug-and-play” approaches do not need a simplified model for the building dynamics but, instead, they are “plugged” directly either to an elaborate model for the building dynamics or to the actual building and they accomplish the BOC design automatically. More precisely:
- •
Model-based “plug-and-play” BOC designs utilize available BEPS models for assessing the energy performance of the controlled building [17], [18], [19], [20], [21]. A BEPS model of the building is connected to a parameterized BOC system; the significant advantage of model-based designs is that an optimizer updates the parameters of the BOC system by evaluating them via the BEPS model, possibly using realistic conditions and physical constraints that occur during real-life building operations.
- •
Model-free “plug-and-play” BOC designs proceed one step further by avoiding the need for a BEPS model of the building [22], [23], [24], [25], [26], [27], [28], [29]. Instead, they are plugged directly into the actual building and they accomplish the control design “on the fly”, i.e., while the building is in operation. Model-free designs possess the significant advantage over model-based ones that they do not inherit inaccuracies of the model: in fact, no matter how elaborate a BEPS model is, the presence of inaccuracies is inevitable and, due to wearing and aging phenomena, inaccuracies become larger as times evolves.
Despite the above-mentioned advantages, the development of efficient model-based and model-free “plug-and-play” BOC systems faces the following challenges:
- i.
In the case of model-based BOC designs, the challenge is to employ optimizers that can operate over elaborate building simulation models. Unfortunately, even for buildings of small size (e.g., with 4–5 rooms), the complex interactions between the different building elements might negatively affect the efficiency of most state-of-the-art optimizers. In general, designing an efficient BOC system becomes a hard optimization task as it requires optimizing over hundreds of parameters that are mutually interacting in a nonlinear and complex manner.
- ii.
In the case of model-free BOC designs, the challenge is to embed the design with efficient self-tuning mechanisms for adapting in real-time the BOC parameters. In fact, the incorporation of adaptation introduces a learning transient of – sometimes – long duration that may lead to non-acceptable performance. As a matter of fact, the available results in adaptive and self-tuning control have exhibited an efficient performance only in systems of small-scale with linear or linear-like dynamics [30], [31]. However, buildings have highly multivariable nonlinear dynamics.
Recently, we have introduced and analyzed a novel “plug-and-play” control design approach – namely Parametrized Cognitive Adaptive Optimization (PCAO) [32], [33] – which is applicable to both model-based and model-free designs and which successfully addresses the challenges described above. The purpose of this paper is to report on the results of the implementation of PCAO for the design of model-based and model-free “plug-and-play” BOC systems. Simulation and real-life experiments conducted on a 10-office building are reported. The experiments show the capability of PCAO to efficiently cope with a large number of parameters that are mutually interacting in a highly nonlinear manner. Furthermore, the PCAO self-tuning mechanisms are capable of adapting the control system in a safe manner without introducing poor transient performance.
The paper is organized as follows: Section 2 presents the building that served as the test case for running the reported experiments. The PCAO BOC algorithm is presented in Section 3. Sections 4 Simulation experiments, 5 Real-life experiments collect the simulation and real-life experiments performed on the test case. The experiments have been performed as part of a wider effort, the FP7-funded research project AGILE. The interested reader can find more information on [34], [35] or http://www.agile-fp7.eu/. Section 6 summarizes the main findings and concludes the paper.
Section snippets
Test case and BOC objectives
The test case for evaluating the proposed BOC systems is a 10-office building (see Fig. 1) located at the Technical University of Crete in Chania, Greece. All the offices of the test case building are equipped with indoor temperature, humidity and occupancy sensors, and split type HVAC units. The building test case covers a surface of 450 m2: the annual energy consumption of the building for HVAC operation is 130 kW h/m2 (based on energy audits), which means 58,500 kW h every year and on average 160
PCAO concept and main principles
Many control problems can be formulated as optimization problems aiming at minimizing an objective function which is related to the system performance. In case of BOC system design, the problem is the one of minimizing the total score (1) subject the building dynamics. Contrary to many optimization problems, where the systems dynamics are assumed to be known and described by a state-space analytical model, in the building case such assumptions are rather restrictive. Building models based on
Simulation experiments
In this and in the next section, we provide the results of extensive simulation as well as real-life experiments performed on the test case building (on its EnergyPlus model, and on the real building itself) under different BOC strategies. The model-based and the model-free PCAO BOC system will be hereafter shortened with MB-BOC and MF-BOC, respectively. Please notice that all the data in the plots and in the tables refer to the daily mean energy and discomfort averaged to one office, which
Real-life experiments
Several real-life experiments were conducted in the test case building during summers of 2012 and 2013. For the real-life experiments, the sensor/actuator infrastructure deployed as part of the FP7-funded project PEBBLE [36] was utilized. In all real-life experiments, only the PCAO–BOC system with L = 1 has been tested. Since the interest of the experiments was on reduced energy consumption, only the RB-BOC2 (with 25 °C) was used for comparisons. The main challenges in evaluating real-life
Conclusions
The main conclusions drawn from both the simulation and real-life experiments are:
- •
The model-based PCAO BOC design is able to provide efficient solutions to large-scale BOC problems where state-of-the-art optimization-based designs are at stake or totally fail. Different performance measures have been adopted to show consistent improvements in front of weather variability;
- •
The model-free PCAO BOC design is able to learn the nearly-optimal BOC strategy “on the fly”, directly from measurements
Acknowledgements
The research leading to these results has been partially funded by the European Commission FP7-ICT-5-3.5, Engineering of Networked Monitoring and Control Systems, under the contract #257806 AGILE – http://www.agile-fp7.eu/ and FP7-ICT-2013.3.4, Advanced computing, Embedded Control Systems #611538 Local4Global – http://www.local4global-fp7.eu/.
References (54)
- et al.
Management and monitoring of public buildings through ICT based systems: control rules for energy saving with lighting and HVAC services
Front Archit Res
(2013) - et al.
USE of the ANOVA approach for sensitive building energy design
Appl Energy
(2010) - et al.
Predictive controllers for thermal comfort optimization and energy savings
Energ Buildings
(2008) - et al.
Use of model predictive control and weather forecasts for energy efficient building climate control
Energ Buildings
(2012) - et al.
Optimizing building comfort temperature regulation via model predictive control
Energ Buildings
(2013) - et al.
Model predictive HVAC load control in buildings using real-time electricity pricing
Energ Buildings
(2013) - et al.
Trial results from a model predictive control and optimization system for commercial building HVAC
Energ Buildings
(2014) - et al.
Use of model predictive control for experimental microgrid optimization
Appl Energy
(2014) - et al.
Building energy performance analysis by an in-house developed dynamic simulation code: an investigation for different case studies
Appl Energy
(2014) - et al.
Advanced control systems engineering for energy and comfort management in a building environment – a review
Renew Sustain Energy Rev
(2009)
Contrasting the capabilities of building energy performance simulation programs
Build Environ
EnergyPlus: creating a new-generation building energy simulation program
Energ Buildings
Optimization of building thermal design and control by multi-criterion genetic algorithm
Energ Buildings
Improved thermal building management with the aid of integrated dynamic HVAC simulation
Build Environ
Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer
Appl Energy
Indoor air-quality control by a fuzzy-reasoning machine in naturally ventilated buildings
Appl Energy
Intelligent building research: a review
Autom Constr
A new adaptive PI controller and its application in HVAC systems
Energy Convers Manage
Intelligent building energy management system using rule sets
Build Environ
Neural networks based predictive control for thermal comfort and energy savings in public buildings
Energ Buildings
Intelligent demand side energy management system for autonomous polygeneration microgrids
Appl Energy
Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings
Appl Energy
Adaptive control strategies for single room heating
Energ Buildings
A review on buildings energy consumption information
Energ Buildings
Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings
Appl Energy
The gap between predicted and measured energy performance of buildings: a framework for investigation
Autom Constr
Cited by (68)
Assessment of buildings’ dynamic thermal insulation technologies-A review
2022, Applied EnergyModel predictive control strategy using encoder-decoder recurrent neural networks for smart control of thermal environment
2021, Journal of Building EngineeringData requirements and performance evaluation of model predictive control in buildings: A modeling perspective
2021, Renewable and Sustainable Energy ReviewsCitation Excerpt :Consequently, extracting the relationship between model and control performance involves explicitly designed experiments, which is rare in the past years. Several studies showed that model mismatch could result in more energy consumption [119,132] and/or discomfort [97,139]. To quantify, 10% error led to 5% more energy cost and 100% more comfort violation [28].
Deep reinforcement learning for energy management in a microgrid with flexible demand
2021, Sustainable Energy, Grids and NetworksCitation Excerpt :Model-free or data-driven approaches consist of learning abstract representations of near-optimal control strategies in the microgrid from its operational data. Learning-based methods have been introduced in recent years as an alternative to model-based approaches, as they can reduce the need for an explicit system model, improve the EMS scalability, and reduce the maintenance costs of the EMS [21]. One of the most promising learning-based EMS methods is the reinforcement learning (RL) paradigm [22], in which an agent learns the dynamics of the microgrid by interacting with its components.
Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review
2021, Applied Thermal Engineering
- 1
The first two authors have equally contributed to this work.