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Model predictive HVAC load control in buildings using real-time electricity pricing

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... DSM and AC control for optimal energy usage are significant ways to handle global energy challenges. DR measures in addition to control approaches can be applied [55]. In existing literature, several thermal models have been developed in addition to control strategies for AC-based DR programs and are discussed below. ...
... MPC for ACs that incorporated thermal energy storage of buildings has also been discussed in [96] and [97]. RTP incorporation into the MPC to handle ACs energy usage is introduced in [55]. Nonlinear MPC approach has been applied [98] to maintain the thermal comfort of residents and ACs energy minimization. ...
... Modelling of optimal DR control [51] Dry bulb temperature/relative humidity measurement [170] AC load profiles in one second resolution [245] Stochastic dynamic programming [66] Distributed control algorithm [228] MILP [218] Termal model of grey-box room in combination with optimization techniques [101] THIC [246] Novel THIC [184], [185] Novel THIC and its associated control [186] THIC for moisture and temperature control [182] SSPCM in building [189] Fuzzy expert system [121] Utilizing temperature set backs and energy efficient measures (EEMS) [179] MPC [55] Program of DLC [198]- [200] Queuing system modelling [233] Strategy of robust AC sequence [146] Fast DEM and response control approach [231] Control of direct power limit [232] Control of AC usage according to number of people in a building by measuring CO 2 [ The sales of ACs have increased worldwide over the last few years. AC usage is dependent on several social, economic and climatic factors. ...
Article
Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand of end-users. Demand response (DR) has acquired considerable attention due to uncertain generation outputs from intermittent renewable energy sources and advancements of smart grid technologies. The percentage of the air-conditioner (AC) load over the total load demand in a building is usually very high. Therefore, controlling the power demand of ACs is one of significant measures for implementing DR. In this paper, the increasing development of ACs, and their impacts on power demand are firstly introduced, with an overview of possible DR programs. Then, a comprehensive review and discussion on control techniques and DR programs for ACs to manage electricity utilization in residential and commercial energy sectors are carried out. Next, comparative analysis among various programs and projects utilized in different countries for optimizing electricity consumption by ACs is presented. Finally, the conclusions along with future recommendations and challenges for optimal employment of ACs are presented in the perspective of power systems.
... Model predictive control (MPC) is becoming a established automation technology in HVAC central plants [3,[9][10][11][12][13]. MPC can anticipate and counteract disturbances and accommodate complex models, constraints, and cost functions [3,14,15]. ...
... If E j,t+1 ≤ 0, set E j,t+1 = 0, E j,t+1 = 0, E j,t+1 = (1 − β)E j , and update ul j,k+1 = ul j,k+1 − E j,t+1 . 9. If the current prediction horizon T spans a single month, update the carry over demand charge R t+1 = max{R t , r e t }, else update as R t+1 = (max{R t , r e t+1 }, 0) with r e t+1 being the actual realized residual electrical demand calculated from Eq. (2.5b) using actual realized electrical load L e t+1 (ξ). ...
Preprint
We present a stochastic model predictive control (MPC) framework for central heating, ventilation, and air conditioning (HVAC) plants. The framework uses real data to forecast and quantify uncertainty of disturbances affecting the system over multiple timescales (electrical loads, heating/cooling loads, and energy prices). We conduct detailed closed-loop simulations and systematic benchmarks for the central HVAC plant of a typical university campus. Results demonstrate that deterministic MPC fails to properly capture disturbances and that this translates into economic penalties associated with peak demand charges and constraint violations in thermal storage capacity (overflow and/or depletion). Our results also demonstrate that stochastic MPC provides a more systematic approach to mitigate uncertainties and that this ultimately leads to cost savings of up to 7.5% and to mitigation of storage constraint violations. Benchmark results also indicate that these savings are close to ideal savings (9.6%) obtained under MPC with perfect information.
... There are a of number current attempts to use modeling approaches for control in commercial buildings [10][11][12]. These are typically based on thermal models that are attempting to capture the details of all the thermal interactions in buildings [12]. ...
... The modeled indoor temperature compared to the "actual" indoor temperature from single zone building simulation from the EnergyPlus is shown in Figure 5. The percentage error found between the actual indoor temperature from simulation to our model is 6.3512% using Equation (11) to calculate error. This initial result is promising but we can extend it even further. ...
Article
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We investigate data-driven, simple-to-implement residential environmental models that can serve as the basis for energy saving algorithms in both retrofits and new designs of residential buildings. Despite the nonlinearity of the underlying dynamics, using Koopman operator theory framework in this study we show that a linear second order model embedding, that captures the physics that occur inside a single or multi zone space does well when compared with data simulated using EnergyPlus. This class of models has low complexity. We show that their parameters have physical significance for the large-scale dynamics of a building and are correlated to concepts such as the thermal mass. We investigate consequences of changing the thermal mass on the energy behavior of a building system and provide best practice design suggestions.
... At the local control level, the authors of [19] proposed a model predictive HVAC load control strategy that was aimed at reducing energy consumption as well as minimising deviations of indoor temperature from preferred values. They developed a setpoint-price assignment algorithm that captures a consumer's attitude towards thermal comfort to determine reference temperatures for a 24 h planning horizon. ...
... In Equation (19), the time units are reported in minutes, giving a time-constant of one hour and a time delay of six minutes. The heater was fast-responding, and hence there were no rate constraints associated with the control input signal; the control input signal u(t) was amplitude constrained in the range 0-100% within the process model G(s) block in Figure 3. Experimental analysis in previous work showed that the gain, the time constant and, the time delay can vary in the real system, within range (72-84), (60-80), and (5-7), respectively [30]. ...
Article
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Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.
... The terms α(k) and Φ(k) are sequences that will control the future response of the modeled plant. These multi-objective functions mostly focused on energy consumption [33,109,[113][114][115][116][117][118][119][120][121][122][123], thermal comfort Parameters (PPD, PMV etc.) [95][96][97], energy cost [124][125][126][127], both energy cost and demand cost [128][129][130], and deviation of indoor temperature [96,115,131]. Most of the objectives functions are particularly related to energy cost and thermal comfort parameters. ...
... The terms α(k) and Φ(k) are sequences that will control the future response of the modeled plant. These multi-objective functions mostly focused on energy consumption [33,109,[113][114][115][116][117][118][119][120][121][122][123], thermal comfort Parameters (PPD, PMV etc.) [95][96][97], energy cost [124][125][126][127], both energy cost and demand cost [128][129][130], and deviation of indoor temperature [96,115,131]. Most of the objectives functions are particularly related to energy cost and thermal comfort parameters. ...
Article
Building systems are subject of dynamic system that have a general feature of non-linearity and in turn, present us with different challenges for its optimized control of energy-saving and thermal comfort. Occupancy behavior, weather forecast, ambient temperature and solar irradiation, etc. In particular are difficult to predict. These uncertainty parameters have a direct influence on the building's behavior that further complicate problem formulation for energy saving in a building. Model predictive control (MPC) has been one of the potential strategies for control schemes to address these problems and tackle them since its invention. MPC is a suitable and the best candidate when it comes to questioning for future predictions in terms of energy efficiency, cost, and control mechanisms. MPC consists of model of a plant, prediction horizon and optimization tools used for the optimization of the future response of the plant. After broad applications of MPC in industrial applications for process control, it has been gaining ground in the field of Heating Ventilation and Air-conditioning (HVAC). Although there has been extensive research of MPC in HVAC systems of buildings, there lacks a detailed review, a complete structure that formulates and describes the applications. An overall holistic view of applications of MPC in building HVAC system has been provided in this paper. Broader information on modeling techniques and optimization algorithms are discussed in a detailed manner. Different design parameters such as prediction horizon, time step, cost function, etc., that ultimately affect MPC performance are presented in a comprehensive form. Various kinds of modeling software with their technical features, pros and cons are elaborated. The main goal of the current paper is to highlight important design parameters crucial for the MPC control scheme and provide better guidelines for further studies. Various future outlines have been listed that can be helpful for future work in this field.
... Moreover, most of the state of the art load management techniques are based on forecasting models [13][14][15]. Such applications take advantage of the periodicity in the load to model the behavior and manage the loads in consequence. ...
... However, in scenarios where the refrigeration machinery cannot be modified due to economical or process restrictions, the refrigeration systems are commonly improved by manipulating the generation part, composed by the compressors and the condensers, or modifying the temperatures of operation, the evaporation and condensation [8][9][10][11][12]. Nevertheless, by manipulating the load side, composed by the evaporators of the refrigeration system, it is also possible to improve the efficiency of the refrigeration system in an indirect way [13][14][15]. element in both circuits, three pumps to force the liquid recirculation, and various evaporators distributed in the eight spaces to refrigerate (S1-S8) of the facility. The refrigerant employed in the overfeed refrigeration system is the ammonia (R717), due to its efficiency in low temperatures, its environmentally friendly properties, its heat transfer, and its low price. ...
Article
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A common denominator in the vast majority of processes in the food industry is refrigeration. Such systems guarantee the quality and the requisites of the final product at the expense of high amounts of energy. In this regard, the new Industry 4.0 framework provides the required data to develop new data-based methodologies to reduce such energy expenditure concern. Focusing in this issue, this paper proposes a data-driven methodology which improves the efficiency of the refrigeration systems acting on the load side. The solution approaches the problem with a novel load management methodology that considers the estimation of the individual load consumption and the necessary robustness to be applicable in highly variable industrial environments. Thus, the refrigeration system efficiency can be enhanced while maintaining the product in the desired conditions. The experimental results of the methodology demonstrate the ability to reduce the electrical consumption of the compressors by 17% as well as a 77% reduction in the operation time of two compressors working in parallel, a fact that enlarges the machines life. Furthermore, these promising savings are obtained without compromising the temperature requirements of each load.
... In the last decade, there has been a surge in the amount of research focused on multi-objective controllers. The application of data-driven controllers [21,22], and model predictive controllers (MPCs) [23][24][25][26] has shown significant accuracy in controlling the multiple objectives. However, buildings are heterogeneous systems (complex networks of appliances and sensors) and the necessity of multi-objective applications for controllers made their design and development extremely challenging and time-consuming. ...
Article
Full-text available
The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency, on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, computationally low cost, and of moderate modeling complexity. Furthermore, there is no standard model configuration, model development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, configurations, parametric identification techniques, and influential parameters.
... Model predictive control (MPC) is becoming an established automation technology in HVAC central plants [3,[9][10][11][12][13] . MPC can anticipate and counteract disturbances and accommodate complex models, constraints, and cost functions [3,14,15] . ...
Article
We present a stochastic model predictive control (MPC) framework for central heating, ventilation, and air conditioning (HVAC) plants. The framework uses real data to forecast and quantify uncertainty of disturbances affecting the system over multiple timescales (electrical loads, heating/cooling loads, and energy prices). We conduct detailed closed-loop simulations and systematic benchmarks for the central HVAC plant of a typical university campus. Results demonstrate that deterministic MPC fails to properly capture disturbances and that this translates into economic penalties associated with peak demand charges and constraint violations in thermal storage capacity (overflow and/or depletion). Our results also demonstrate that stochastic MPC provides a more systematic approach to mitigate uncertainties and that this ultimately leads to cost savings of up to 7.5% and the mitigation of storage constraint violations. Benchmark results also indicate that these savings are close to ideal savings (9.6%) obtained under MPC with perfect information.
... Subsequently, using an economic objective has major potential if electricity-based supply systems such as heat pumps and chillers are used. The advantage of these objectives has been widely studied in the context of demand-response problems with real-time pricing (Avci, Erkoc, Rahmani, & Asfour, 2013;Bianchini, Casini, Vicino, & Zarrilli, 2016a). It has been shown that economic optimization could be used to reduce the peak electricity demand (Oldewurtel, Ulbig, Parisio, Andersson, & Morari, 2010b), or increase the stability, flexibility, and sustainability of the energy system, particularly in the face of growing intermittent renewable generation (Patteeuw, Henze, & Helsen, 2016;Qureshi & Jones, 2018). ...
Article
Full-text available
It has been proven that advanced building control, like model predictive control (MPC), can notably reduce the energy use and mitigate greenhouse gas emissions. However, despite intensive research efforts, the practical applications are still in the early stages. There is a growing need for multidisciplinary education on advanced control methods in the built environment to be accessible for a broad range of researchers and practitioners with different engineering backgrounds. This paper provides a unified framework for model predictive building control technology with focus on the real-world applications. From a theoretical point of view, this paper presents an overview of MPC formulations for building control, modeling paradigms and model types, together with algorithms necessary for real-life implementation. The paper categorizes the most notable MPC problem classes, links them with corresponding solution techniques, and provides an overview of methods for mitigation of the uncertainties for increased performance and robustness of MPC. From a practical point of view, this paper delivers an elaborate classification of the most important modeling, co-simulation, optimal control design, and optimization techniques, tools, and solvers suitable to tackle the MPC problems in the context of building climate control. On top of this, the paper presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems. The paper draws practical guidelines with a generic workflow for implementation of MPC in real buildings aimed for contemporary adopters of this technology. Finally, the importance of standardized performance assessment and methodology for comparison of different building control algorithms is discussed.
... IBDR includes direct load control, emergency demand response program, demand bidding program, capacity market program, interruptible tariffs, and ancillary service markets [40]. For the IBDR program, there are fixed or time-varying incentives and specific constraints to administrate the consumer's appliances remotely [41,42]. The consumers are expected to reduce their power consumption during the peak hours of high system stress [43]. ...
Article
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Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer's flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.
... Cost savings Peak power reductions Grey-box room thermal model in combination with optimization techniques [113] Hong Kong (Summer day) 26% power reduction Residents' thermal comfort DR based OPTIGES project [114] Spain Losses minimization Environmental benefits Model Predictive Control (MPC) [115] -Reduce total energy consumption Residents' thermal comfort Commercial Energy Sector Novel THIC and its associated control [116] Museum ...
Article
Nowadays, the attainment of flexibility in different loads for different electricity customers is an interesting topic. The assessment of flexibility is important because of the ultimate benefits to handle challenges for both electricity customers and utilities. At first, different loads that have significant contribution in energy consumption and may be potential candidates for the extraction of demand flexibility are discussed in this paper. Secondly, different descriptions of demand side flexibility and its assessment manipulation on individual and aggregate loads are explored. After investigation of different techniques some innovative definitions of flexibility indices are surveyed for numerical flexibility assessment. The information about the flexibility potential can lead to initiate different Demand Side Management (DSM) techniques that is helpful to solve electrical power systems key issues. Keeping in view this aspect, different DSM techniques for Demand Response (DR) programs are also reviewed in this paper.
... The variable room temperature setpoints method is based on the fact that building energy use and peak loads in building cooling applications are inversely related to the space setpoint temperature. Various studies have reported this approach to have beneficial effects on reducing building energy use [8][9][10] and building peak loads [11,12]. ...
Article
Full-text available
Sizing of borehole heat exchangers (BHEs) for direct ground cooling systems (DGCSs) is a critical part of the overall system design. This study investigates the thermal performance and sizing of a DGCS with two different operation strategies using experimental and simulation approaches. The traditional on/off operation strategy keeps a constant room temperature. The continuous operation strategy has the potential to reduce the building peak cooling loads by precooling the space and having a variable room temperature measures. The experimental results from the laboratory-scale setup show the differences in the hourly room heat extraction rates and the room temperature pattern for the operation strategies applied. The experimental data is also used to develop a simulation model. The simulation results show that applying the continuous strategy reduces the building peak cooling loads and lowers the heat injection rates to the ground. For new BHEs, applying the continuous strategy can result in shorter BHEs, owing to the significantly lower ground heat injection rates. For existing BHEs, applying the continuous strategy can decrease the borehole outlet fluid temperature and thus, increase the cooling capacity of the building cooling system. The findings of this study have implications for developing the widespread use of DGCSs.
... Energy management focuses on the intelligent utilization of energy. Demand Response (DR) can be achieved through two programs: incentive based [1] and price based [2]. In incentive-based programs, the utility can wirelessly switch the state of the user's appliance from the on to the off-state by sending a short message to the load unit (LU) or smart building's (SB) energy management controller (EMC) whenever it senses the peak. ...
Article
Full-text available
Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.
... Ultimately, the MELs need to be modified to receive a signal and enter a ''vacancy mode" when the situation is appropriate. Some devices are already equipped to receive a signal in order to respond to real-time electricity prices and to provide other grid services [41]. This paper focuses on the VIE and its ability to infer vacancy from common sensors and then applies it to a case study. ...
Article
Full-text available
Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE’s ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively).
... In sum, there are numerous researches related to the application of MPC in building. All these works highlight the importance of accurate modeling of the building energy system, above all they bear testimony to the capability of MPC for achieving significant amount of financial/energy saving without compromising the comfort level of residents [24,25,26]. Although a variety of approaches have been developed to manage the building's energy system, the existing literature however lacks a framework to treat the question put forward earlier. ...
Preprint
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The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic thermal (PVT) collectors to supply both electric and thermal energy demands of the building with minimum operating cost. To this end, the paper develops a stochastic model predictive control (MPC) strategy to optimally determine the set-point of the whole building energy system while accounting for the uncertainties associated with the PVT energy generation. This system enables the building to 1-shift its electric demand from high-peak to off-peak hours and 2- sell electricity to the grid to make energy arbitrage.
... It was demonstrated that there were significant potential energy savings. More studies related with the application of MPC in different buildings may be found in the literature (Alimohammadisagvand et al., 2016;Salakij et al., 2016;Candanedo et al., 2013;Avci et al., 2013) and all of them conclude that running simulations with a proper working horizon, an acceptable data time step, and a correct modelling of the system provide important economic savings compared to the standard set-ups with ON/OFF control. A set-up with PV panels, a heat pump, an electrical battery, slab cooling, and also an electric water heater was optimized by Vrettos et al. (2013) through a MPC strategy, using an hourly time step and a prediction horizon of 16 hours. ...
... Maasoumy et al., 2014) reported is 45 % in mid-season and 30%-40% in heating mode, respectively by comparison of MPC with two conventional control strategies in an experimental assessment (Viot, Sempey, Mora, Batsale, & Malvestio, 2018). For electrical energy consumption, (Avci, Erkoc, Rahmani, & Asfour, 2013) attained 44.2 % of reduction in energy consumption by load control of HVAC with MPC in peak-shaving mode and 14.5 % in cooling mode. Additionally (Hazyuk et al., 2014) reported 18.2 % of electrical energy saving by comparison of MPC vs. scheduled PID in heating mode and 18.3 % in a typical mid-season. ...
Article
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High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations.
... DSM mainly focuses on the usage of smart energy resources. DR uses two methods: Incentive-based [5] and price-based [6]. In the incentive-based method, consumers' devices are interrupted to an ON / Off state by sending a quick note to the smart house (SH) or Smart Building (SB). ...
Article
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As the world population and its dependency on energy is growing exponentially day by day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers’ waiting times, due to scheduling of appliances.
... An hourly-variant electricity price is directly considered in the MPC cost function. Likewise, Avci et al. (2013) proposed an efficient MPC-based approach to air conditioning system participation in DSM under dynamic pricing. Hu et al. (2019) investigated the application of MPC for buildings' heating system control based on weather, occupancy, and dynamic prices. ...
Article
Buildings are responsible for a large portion of the world’s energy consumption. Any measure that can be taken to optimize the use of energy related to them must be considered. Demand Side Management (DSM) can be used to shave demand peaks and to avoid bootstrapping highly polluting fast ramp-up generators. This though brings a control problem that is complicated by the increasing diffusion of small-scale, renewable energy sources and local storage facilities which are decentralized and, in general, hard to predict reliably. The overall goal of the control strategy is to balance energy, demand/supply, and to minimize costs. This survey focuses on control strategies to support DSM, considering buildings as the load to be managed. Among the various control strategies, model predictive control (MPC) has a predominant role due to its broad applicability and easy portability to many diverse contexts. The method is suitable for any nonlinear, multi-variable, and linear parameter varying system. The survey provides a general, unifying mathematical characterization of the approaches and lays the foundations for comparing and evaluating MPC-based DSM in buildings.
... Hence, it is very important from an economic and environmental point of view to implement policies to reduce HVAC energy consumption. Control strategies can be very effective and low-cost policies for reducing energy consumptions [18][19][20][21][22][23]. The work presented in this paper is aimed at an investigation of design and control strategies to reduce energy consumption and to propose a cleaner source of energy to power Princess Nourah Bint Abdulrahman University in Riyadh, Kingdom of Saudi Arabia Automated People Mover (PNU-APM). ...
Article
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It is broadly acknowledged that there is an urgent need to reduce carbon-based mobility systems and increase renewable energy alternatives. The automotive industry is one of the greatest consumers of energy in the world. It is fronted with many challenges that aim at reducing carbon emissions. Renewable energy costs are getting cheaper and more cost effective. However, well devised design and control strategies are also needed in order to optimize any systems that are adopted in this field. Previous research shows that the energy consumption for non-traction purposes may be of the same scale as the energy used to move rolling stock, and in some cases even larger. The Kingdom of Saudi Arabia is very interested in the implementation of policies that aim at reducing energy consumption and encouraging renewable energy programs. Under its Vision 2030 development program, the Kingdom of Saudi Arabia is looking to produce 30% of its energy from renewables and other sources, with solar energy playing an important role. The work presented in this paper is aimed at an investigation of design and control strategies to reduce energy consumption and to propose a cleaner source of energy to power Princess Nourah Bint Abdulrahman University’s Automated People Mover (PNU-APM). Two areas of applications have been investigated for adopting these types of technology. Firstly, a p-v solar energy option that could be adopted for implementation in potential applications since the metro system is already in full operation using electricity. Secondly, design and control strategies including exploiting solar energy for a metro operation are discussed and investigated. A number of strategies to reduce heating, ventilation, and air conditioning (HVAC) load, which happens to be the biggest energy consumer, have been discussed. Results show great potential in energy savings with adopting p-v solar sources as well as implementation of few suggested control strategies. Some deliberations of some of the drawbacks of solar energy are also offered.
... Heating, ventilation, and air conditioning (HAVC) systems are responsible for more than 30 percent of energy consumption in buildings [1]. The HVAC system consists of chillers, terminal units, air handlers, condensers, etc., where chillers have the highest energy usage in comparison with other components. ...
... Minimizing the total energy use (Picard and Helsen 2018;Jorissen 2018) and cost (Bianchini et al. 2016;Avci et al. 2013;Vrettos et al. 2013) are the most frequent motivations for MPC in buildings. Economic MPC could be applied to effectively reduce the peak electricity demand (Oldewurtel et al. 2010) or to increase the building energy flexibility (Patteeuw, Henze, and Helsen 2016). ...
Article
This paper presents a methodology for development of control-oriented thermal RC network models for optimized HVAC load management in typical electrically heated single-family two-storey detached houses in Québec, assuming that there are three zones and it is equipped with photovoltaics and battery storage. Using data from an unoccupied research house, two 3rd order RC networks are developed and calibrated. One model (3C6R network) assumes that each floor is a separate thermal zone, and the other model (3C7R network) assumes that the south-facing zone and the north-facing zone of the building are separate zones. Application of model predictive control with both developed models results in an average 12.1% reduction in the daily heating load, 19.8% reduction in the total daily electricity imported, 68.1% reduction in the peak demand, 67.0% reduction in the energy cost, and 13.4% increase in the self-consumption of on-site generated solar electricity compared to a traditional reactive controller.
... Thus, it is better to predict the "best performance model" in advance using the "metalearning" strategy. The perspective of users, the user requirements, the maximum accuracy that meter equipment can provide and the relationships between these factors have a high impact on the function of load prediction and, thus, the user preferences [44,45] regarding load predictions should be considered. Two-stage user preferences are investigated in this study, which includes two aspects and one restriction; the two aspects are the subjective requirements of the approach for the prediction or monitoring systems and the accuracy requirements for the prediction system; the restriction is an investment. ...
Article
Building data forecasting plays an increasingly important role in building energy savings. However, the one-fits-all model cannot satisfy all the requirements of multiple application scenarios and user preferences. Motivated by the need to bridge the research gap between different user preferences (application scenarios) and energy prediction model recommendation systems, this paper proposes a novel meta-learning strategy based on an artificial neural network recommendation system. This strategy is employed for real-time cooling loads, coefficients of performance prediction and optimal prediction model recommendations. The data set is composed of 40 cases from five factory buildings. After the predictions and recommendations are obtained for all cases, the two-stage user preferences are considered based on multi-objective decision-making algorithms. Then, a new model termed the “walking slide method”, is proposed to predict some special cases. This study shows that the seasonal autoregressive integrated moving average model and random forest model achieve the best prediction accuracy and the minimum computation cost separately for most cases, while the long short-term memory is the best model when considering the two criteria. The variances between the different cases lead to a lower cross-validation score (approximately 65%), but a higher success rate (over 99%) for the recommendation performance. In addition, in the more complex application scenarios, a lower prediction accuracy and recommendation success rate will be obtained. In most cases, the use of a prediction combined with a monitoring system is the best choice. Last, the reliability of the results is verified by application studies. This work provides a scientific basis for energy prediction applications based on user preferences.
... In sum, there are numerous researches related to the application of MPC in building. All these works highlight the importance of accurate modeling of the building energy system, above all they bear testimony to the capability of MPC for achieving significant amount of financial/energy saving without compromising the comfort level of residents [24,25,26]. Although a variety of approaches have been developed to manage the building's energy system, the existing literature however lacks a framework to treat the question put forward earlier. ...
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The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic thermal (PVT) collectors to supply both electric and thermal energy demands of the building with minimum operating cost. To this end, the paper develops a stochastic model predictive control (MPC) strategy to optimally determine the set-point of the whole building energy system while accounting for the uncertainties associated with the PVT energy generation. This system enables the building to 1-shift its electric demand from high-peak to off-peak hours and 2- sell electricity to the grid to make energy arbitrage.
Article
Optimizing the scheduling of heating, ventilation, and air-conditioning (HVAC) systems in multizone buildings is a challenging task, as occupants in various zones have different thermal preferences dependent on time-varying indoor and outdoor environmental conditions and price signals. Price-based demand response (PBDR) is a powerful technique that can be used to handle the aggregated peak demand, energy consumption, and cost by controlling HVAC thermostat settings based on time-varying price signals. This paper proposes an intelligent and new PBDR control strategy for multizone office buildings fed from renewable energy sources (RESs) and/or utility grid to optimize the HVAC operation considering the varying thermal preferences of occupants in various zones as a response of real-time pricing (RTP) signals. A detailed mathematical model of a commercial building is presented to evaluate the thermal response of a multizone office building to the operation of an HVAC system. The developed thermal model considers all architectural and geographical effects to provide an accurate calculation of the HVAC load demand for analyses. Further, Occupants' varying thermal preferences represented as a coefficient of a bidding price (chosen by the occupants) in response to price signals are modeled using an artificial neural network (ANN) and integrated into the optimal HVAC scheduling. Furthermore, a control mechanism is developed to determine the varying HVAC thermostat settings in various zones based on the ANN prediction model results. The effect of the proposed strategy on aggregator utility with wider implementation of the developed mechanism is also considered. The optimization problem for the proposed PBDR control strategy is formulated using a building's thermal model and an occupant's thermal preferences model, and simulation results are obtained using MATLAB/Simulink tool. The results indicate that the proposed strategy with realistic parameter settings shows a reduction in peak demand varying from 7.19% to 26.8%, contingent on the occupant's comfort preferences in the coefficient of the bidding price compared to conventional control. This shows that the proposed approach successfully optimizes the HVAC operation in a multizone office building while maintaining the preferred thermal conditions in various zones. Moreover, this technique can help in balancing the energy supply and demand due to the stochastic nature of RESs by cutting electricity consumption.
Article
In this paper, an integrated optimal scheduling and predictive control scheme with a hierarchical structure is proposed for energy management of an urban complex (UC). The proposed scheme consists of a scheduling layer optimizing the energy usage of the UC and a control layer controlling the heating, ventilation, and air conditioning (HVAC) in each individual building. In the control layer, a detailed physical model of the individual building with HVAC system is developed to predict its energy consumption while considering the thermal dynamics of the building envelope with multiple layers of construction material. In the scheduling layer, a multi-objective optimal scheduling is formulated based on the predictive energy consumption of the buildings to reduce the peak-valley load difference and minimize the operating cost of the UC. Finally, the optimal control schedules are obtained and issued to the individual HVACs. Numerical results show that the proposed method can reduce the operating cost and reduce the peak-valley load difference for the UC. Meanwhile, the HVACs can be controlled in an optimal way within the limits of indoor temperature.
Article
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Several studies have indicated that Model Predictive Control (MPC) of space heating systems can utilize the thermal mass of residential buildings as short-term thermal storage for various demand response purposes. Realization of this potential relies heavily on the accuracy of the model used to represent the thermodynamics of the building. Such models, whether they are grey box or black box, are calibrated using relevant data obtained from initial measurements, and the performance of the calibrated model is validated using data from a subsequent period. However, many studies use validation periods with weather conditions similar to those of the calibration period. Only a few studies investigate whether the calibrated model performs satisfactory when subjected to significantly different conditions. This paper presents data from a simulation-based study on the effect of seasonal weather changes on the performance of a black-box model. The study was conducted using 11 years of Danish weather data (2008-2018). The results indicate that the performance of the black-box model deteriorate as the weather data conditions become increasingly different from those used in the initial model calibration. Further, the results show that calibration in heating season leads to satisfactory model performance through the heating season, but lower performance in transitional seasons (especially spring). Results also show that calibration in February led to highest model performance through heating season, while calibration in March led to satisfactory model performance in the whole heating and fall season.
Article
With the wide application of consumer's electric appliances with flexible power supply or start time and competitive energy markets, it is necessary for the residential consumers to optimize the power scheduling strategy. The existing methods require consumer's awareness, flexibility, and timely responsiveness which remain an obstacle due to the impacts on the lifestyle and preference of consumers. To overcome the obstacle, this paper proposed a residential load scheduling algorithm based on cost efficiency (CE) and consumer's preference (CP) for demand response in smart grid. In this proposed method, the CP is modeled based on the load scheduling framework, the CE based on CP is modeled to measure the effectiveness of consumption expenditure and the relationship between the CE and CP is investigated. Meanwhile, by analyzing the consumer's lifestyle, we develop a load scheduling algorithm based on CE and CP to effectively reflect and affect user's consumption behavior and achieve the optimal energy consumption profile. The method is demonstrated using real data sets. The simulation results show that there is a positive correlation between CE and CP. The proposed method realizes a desired trade-off between the economic efficiency and CP, helping the consumers to enjoy convenience while saving costs.
Article
Two thirds of the total buildings final energy are used for heating purposes, specifically during the peak period. There is a mismatch between the power generation from renewable energy resources and demand. Thermal energy storage systems have been used not only to fill the gap between supply and demand, but also to take advantage of the time-of-use tariff structures. Nowadays, the application of smart controls to regulate heating systems is growing in popularity. Within this context, a model predictive control strategy to improve the operation of a space-heating system coupled with renewable resources is proposed. This model uses a dynamic approach based on forecasting all energy inputs into the system over a given period of time in advance, before taking any operational decisions. The model predictive control strategy was applied to minimize annual energy costs of the heating system of a detached house located in Puigverd de Lleida (Spain) and, based on a heat pump coupled to a thermal energy storage unit and photovoltaic panels. The results show the potential of the model predictive control with a 24-hour horizon. In that case, energy cost savings of 58% can be achieved, compared to the same heating system without smart control.
Article
As one method of demand-side management, dynamic pricing benefits society by promoting power supply and demand balance, renewable energy consumption, and market efficiency improvement. However, due to the novelty of dynamic pricing, it has not been well promoted among consumers in China. The regulators, electricity retailers and consumers all have a critical role to play in promoting dynamic pricing, and they have both common interests and mutual constraints. Coordinating the interests of the three parties will facilitate the promotion of dynamic pricing. This study establishes an evolutionary game model for these three parties from a bounded rationality perspective and investigates each party's dynamic evolution strategy. The findings indicate that the subsidy from the regulator, the cost of promoting dynamic pricing, the electricity price level, the consumer's responsiveness, and psychological factors all influence the promotion of dynamic pricing. The findings suggest policy recommendations for accelerating the development of dynamic pricing and a healthy electricity retail market.
Article
Heating, ventilation and air conditioning (HVAC) systems play an essential role in demand response (DR) programs. In this paper, a fuzzy controller is designed to adjust the HVAC set-points optimally. The aims of the designed controller are multifold: to save energy, to improve user’s comfort, and reduce HVAC electricity costs. In the current research, indices such as daily energy cost, minimum and maximum home temperature, energy usage, energy usage during peak hours, and user’s comfort are proposed and discussed for the evaluation of HVAC function. In addition, the effect of different pricing schemes such as fixed pricing (FP), time-of-use pricing (TOU), and real-time pricing (RTP), are analyzed. Further, the adaptability of the proposed model enabled us to investigate users with different attitudes toward welfare and cost. Finally, the effects of set-point and dead-band width are discussed. The results show that the proposed controller reaches the pre-determined aims successfully. Abbreviations: HVAC: Heating, ventilation, and air conditioning; FP: Fixed Pricing; TOU: Time Of Use; RTP: Real Time Pricing; PR: The HVAC system energy consumption cost in a day; UC: The total time that the user experiences an uncomfortable situation in a day; T_min: The minimum RealFeel Temperature experienced in a day by the user; T_max: The maximum RealFeel Temperature experienced in a day by the user; ECP: Energy consumed by the HVAC system during peak hours; EC: Energy consumed by the HVAC system during a day
Chapter
Internet of Things (IoT) has been developed as a heterogeneous environment that contains network devices with limited resources. The application of IoT principles in the smart city domain creates new opportunities and requires diligent implementation mechanisms for optimal resource utilization. With time, the IoT applications tend to generate and forward a huge amount of data in the smart cities and require a real-time response from the servers. Due to this, the traditional cloud computing architecture is unable to handle the latency-sensitive applications efficiently, and hence, the FoG architecture has been widely implemented with IoT devices to efficiently retrieve or forward the data. For the comprehensive utilization of the resources in the FoG systems-based smart cities, various energy-aware resource allocation schemes have been discussed in this chapter. The schemes suggest different mechanisms to access the required contents with minimal energy consumptions for the applications that are used in smart cities.
Thesis
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Indoor thermal comfort in residential buildings is usually achieved by tenants manually adjusting fixed temperature set-points; this is known as a ‘static’ method. Prior research has explored automated control of thermal comfort based on the concept of a Predicted Mean Vote (PMV) index, which has been developed to provide a model of perceived human comfort. However, one of the dominant contributions to this index, the Mean Radiant Temperature (MRT), effectively the mean radiant temperature of the surrounding interior surfaces, has either been: 1) inaccurately assumed to be the same as indoor air temperature; and/or 2) costly to implement due to the need for numerous additional sensors. Research is posed to leverage prior work in automatically estimating the R-values of walls and ceilings using a combination of smart WiFi thermostat, building geometry, and historical energy consumption [51] to estimate the MRT with accuracy and thus provide a means to control for comfort, rather than temperature alone. In order to assess the energy saving potential of comfort control for any residence, a machine learning model of the indoor temperature based upon a NARX Neural Network is employed. This model leverages historical thermostat and weather data to develop a means to dynamically predict the interior temperature. With a developed model, it is possible to simulate different temperature set-points on indoor temperature, and thus identify the optimal set-point temperature at all times needed to maintain a reasonable comfort condition. Application of this ideal temperature set-point for minimum human comfort to historical weather data and indoor weather conditions can yield an estimate for minimum cooling energy. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. Based on this research, it is proposed that the approach to estimate MRT can be used to calculate a more accurate PMV value and a better representation of human comfort, without anything more than a smart WiFi thermostat with readily available data. Thus, a control strategy based on this paradigm can both achieve thermal comfort in residential buildings and less energy consumption. In addition, a Model Predictive Controller (MPC) is developed to realize more realistic and sensible control. Compressor protection is also considered in the development of the controller.
Article
Throughout the past few decades, the residential building sector has been responsible for an increasingly large share of primary energy demand (21.2%) in the United States (EIA, 2020). Such high building energy demand from the residential sector will eventually cause both economic and environmental problems. Therefore, this study provides an integrative framework for evaluating the economic value of multiple HVAC systems in the U.S. residential sector. Specifically, this study calculates the payback period for a number of building energy systems by applying two environmental policies, the ‘solar tax credit’ and the ‘Emission Trading Scheme (ETS)’. A two-story single residential building in Detroit, MI was examined using the TRNSYS software tool. The building systems were subdivided into passive (insulation level) and active (HVAC type) systems. The insulation levels were classified into three scenarios depending on their respective U-values (low, medium, and high). Similarly, the HVAC systems were classified into twelve different types: (1) furnace and window cooling unit (benchmark), (2) air-source heat pump and air-conditioner (ASHP+AC), (3) ground-source heat pump (GSHP), (4) variable refrigerant flow (VRF), (5) VRF integrated with PV (PV+VRF), and (6) VRF integrated with PV and battery (PV+ESS+VRF). Each system can be operated with or without a heat recovery system. The results clearly show that the ground-source heat pump (GSHP) and PV-integrated systems (PV+VRF) are far more efficient than other conventional systems in reducing building energy demand, operation cost and GHG emissions. However, in contrast to these findings, the study also shows that both renewable energy systems have relatively long payback periods (8.08 to 20.50 years) along with their high system efficiencies. This clearly demonstrates that evaluating the overall efficiencies of building energy systems without performing additional economic analysis can mislead the public about the notion of ‘building energy optimization’. This could lead to suboptimal decision-making, whereas using the proposed framework would enable economically and environmentally preferable choices. In addition, this study presents support for the widespread application of ETS to the U.S. residential buildings. The growing demand for ETS will reduce the payback periods for many residential HVAC systems, especially by encouraging the greater use of renewable energy sources. In conclusion, since the economic incentive provided by ETS rewards taking an integrative approach to reduce building energy demand, operation cost and GHG emissions, adopting this policy actively at the national level will guarantee not only economic benefits for individual building owners, but also positive environmental impacts for the entire society.
Chapter
This paper studies the impact of consumers' individual attitudes towards load shifting in electricity consumption in an electricity market that includes a single electricity provider and multiple consumers. A Stackelberg game model is formulated in which the provider uses price discounts over a finite number of periods in order to induce incentives for consumers to shift their peak period loads to off-peak periods. The equilibrium outcomes are investigated and the analytical results are derived for this type of market, where not only the response behaviors of independent consumers are diverse but also an individual consumer's valuation of electricity consumption varies across periods. The obtained results demonstrate that consumer sensitivities to price discounts significantly impact price discounts and load-shifts, which are not necessarily monotonic. The authors also observe that a diverse market leads to lower peak-to-average values and provider payoffs compared to a homogenous market unless the latter one is composed of consumers with relatively lower inconvenience costs during the peak periods.
Article
The supply-demand imbalance of electricity increases the operating burden on smart grids, decreases the average efficiency of power generation equipment, and threatens the safe operation of power grids. Residential air conditioning is a flexible load and a major consumer of electricity. Therefore, demand response control can be applied to air conditioners (ACs) to shift their peak energy consumption and save energy. Model predictive control (MPC) is an effective demand response control method. In this study, we analyze the cooling seasonal performance of an inverter AC with MPC. A time-varying MPC was designed and evaluated using a simulation testbed that was constructed using MATLAB. Subsequently, the energy, cost, and temperature control performances of the MPC were analyzed in detail from electricity pricing model, weather conditions and fluctuation of real-time price. The results show that compared to the proportional–integral–derivative (PID) control method, MPC can shift the peak-hour energy consumption by 6.34%–21.60% and reduce the total electricity costs by 13.44%–27.43%, while maintaining indoor thermal comfort during the whole cooling season, and Demand response with MPC control is very suited to hot weather conditions with highly fluctuating RTP. By applying MPC hybrid demand response under real-time price, there are better performances on peak shifting and cost saving.
Chapter
In this chapter, an optimal operation method with a hierarchical structure is proposed for the multi-energy building complex (BC). The proposed method consists of a scheduling layer optimizing the energy usage of the BC and a control layer controlling the heating, ventilation, and air conditioning (HVAC) in each individual smart building. In the control layer, a detailed physical model of the individual building with an HVAC system is developed to predict its energy consumption while considering the thermal dynamics of the building envelope with multiple layers of construction material. In the scheduling layer, a multiobjective optimal scheduling is formulated to reduce the peak-valley load difference and minimize the operating cost of the BC. Finally, the optimal control schedules are obtained and issued to the individual HVACs. Numerical results show that the proposed method can reduce the operating cost and reduce the peak-valley load difference for the BC. Meanwhile, HVACs can be controlled in an optimal way within the limits of indoor temperature.
Article
Demand response (DR) of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids. In this special fast DR event, effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment. This study, therefore, developed a data-driven model predictive control (MPC) using support vector regression (SVR) for fast DR events. According to the characteristics of fast DR events, the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance. Meanwhile, a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls. Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously. Compared with RC-based MPC, the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.
Article
Smart thermostats and home energy management systems (HEMSs) are generally studied separately. However, their joint use can provide a greater benefit. Therefore, this study primarily aims to combine a smart thermostat with a HEMS. The mixed-integer linear programming (MILP)-based HEMS performs day-ahead load scheduling for cost-minimization and provides optimal demand response (DR) and photovoltaic (PV) self-consumption, and the fuzzy logic-based thermostat aims efficient DR of air-conditioning and maintenance of thermal comfort. In the first stage, unlike conventional fixed set-point thermostats, the proposed thermostat defines different set-points for each time interval, by fuzzifying input parameters of electricity prices, solar radiation, and occupant presence, to be used by HEMS. In the second stage, the HEMS schedules the operation of time-shiftable, thermostatically controlled, and power-shiftable (battery energy storage system (BESS), electric vehicle (EV)) loads. The HEMS considers bi-directional power flow between home, BESS, EV, and grid, as well as battery degradation to avoid unnecessary energy arbitrage. The simulation results show that a daily cost reduction of 53.2 % is achieved under time-of-use (TOU) and feed-in tariff rates of Turkey. AC cost is reduced by 24 % compared to conventional thermostats. In a future scenario of real-time pricing (RTP) and dynamic feed-in tariff, vehicle-to-grid (V2G) becomes possible.
Thesis
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Conventional building automation and control (BAC) systems employ reactive feedback control, such as proportional-integral-derivative (PID), which limits the efficiency in managing building energy and dealing with the contrasting need for human well-being and energy efficiency. Model predictive control (MPC), which brings forward prediction and optimization capabilities on the principles of closed-loop control to BAC, gains extensive attention as a solution to such limitations, allowing optimisation of building energy efficiency and indoor climate. This study proposes an MPC system based on a building-physics-based modelling approach to formulating a state-space model (SSM) that captures the building dynamics and quantitative occupant comfort indices. The system incorporates real-time optimization for energy efficiency and occupant comfort, which requires less than 0.1 s for solving the optimisation problem in each control interval. The proposed MPC system was implemented in two test buildings, the Building and Construction Authority (BCA) SkyLab and Lecture Theatre 3 (LT3) on the campus of NTU. The control characteristics and performance of the MPC system were evaluated and compared to conventional BAC systemS under different air-conditioning and mechanical ventilation (ACMV) systems including fan coil unit (FCU), active chilled beam (ACB), air-handling unit (AHU) as well as separate sensible and latent cooling (SSLC) with dedicated outdoor air system (DOAS). The MPC system achieved 15 – 20% of electricity savings for the ACMV systems with improved occupant comfort, compared to conventional BAC systems. The MPC system was further developed to perform coordinated control of multiple building systems (air-conditioning, lighting and shading) by incorporating extra models for predicting indoor lighting power and visual comfort. The MPC system achieved up to 20.3% of building electricity savings with great improvements in thermal and visual comfort. In order to enhance the adaptability of the MPC system to the transient nature of building operation, machine learning (ML) technology was adopted to develop an adaptive ML-based building model, which was initialised by historical building operation data and continuously updated using online building operation data. An adaptive MPC system employing such model was developed and implemented in a test office. The MPC system achieved 59% cooling energy saving compared to the conventional thermostat control. This study also explored the feasibility of developing a full ML controller by supervised learning of the control laws of the MPC system using the building operation data generated by the MPC system. The ML controller retained the high thermal comfort performance and 86% of the energy saving performance of the master MPC system.
Article
This paper considers the problem of network overloading in the power distribution networks of Pakistan, often resulting from the inability of the transmission system to transfer power from source to end-user during peak loads. This results in frequent power-outages and consumers at such times have to rely on alternative energy sources, e.g. Uninterrupted Power Supply (UPS) systems with batteries to meet their basic demand. In this paper, we propose a demand response framework to eliminate the problem of network overloading. The flexibility provided by the batteries at different houses connected to the same grid node is exploited by scheduling the flow of power from mains and batteries and altering the charging-discharging patterns of the batteries, thereby avoiding network overloading and any tripping of the grid node. This is achieved by casting the problem in an optimal control setting based on a prediction of power demand at a grid node and then solving it using a model predictive control strategy. We present a case study to demonstrate the application and efficacy of our proposed framework.
Article
Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. Two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.
Article
Increasing real estate and other infrastructure costs have resulted in the trend of co-working offices where users pay as they use for individual desks. Co-working offices that provide personalized comfort need to address users with potentially widely varying thermal comfort preferences. Providing personalized comfort in cabins separated by physical partitions with neighboring thermal zones or open-plan offices with a single actuator has received attention in the literature. In this article, the problem of minimizing user discomfort in open-plan co-working offices with multiple actuators while being cognizant of the energy consumed is considered. Specifically, the decision problems of assigning users to desks based on their thermal preferences and jointly controlling the multiple actuators are addressed. The non-linearities in the underlying thermodynamic constraints and the seating decision together make the problem computationally hard. A two-step heuristic that addresses these issues is presented. First, using a model that accounts for spatio-temporal thermodynamics, a one-time assignment of users to desks is performed that reduces the thermal resistance faced by the HVAC systems to provide the preferred comfort levels. Next, the setpoints are decided for all actuators to jointly minimize user discomfort by optimization and model-predictive control. Further, scalability is addressed by clustering user preferences and the associated HVAC actuators’ setpoints for the cases where a large number of actuators may be present in the room.
Article
This paper investigates the effect of different control strategies applied to electric thermal storage systems to provide demand response services. These results indicate how policymakers or manufacturers could target the implementation of advanced control on electric thermal storage systems and apply these to households characterised by different occupancy profiles, thereby making demand response initiatives more attractive to end users.
Article
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Office buildings contain large sensor network de-ployments to monitor and maintain their internal envi-ronment. They also consume a significant amount of en-ergy. This paper proposes the use of the use of horizon-tal layering, rather than the current vertical-solution ap-proach, to expose the building data plane and enable in-teroporable software services and applications that mon-itor and control the building environment. We present our instantiation of this approach, which includes a data plane (sMAP) and storage service (IS4). Furthermore, we describe a set of applications built in this ecosystem.
Conference Paper
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A method to reduce peak electricity demand in building climate control by using real-time electricity pricing and applying model predictive control (MPC) is investigated. We propose to use a newly developed time-varying, hourly-based electricity tariff for end-consumers, that has been designed to truly reflect marginal costs of electricity provision, based on spot market prices as well as on electricity grid load levels, which is directly incorporated into the MPC cost function. Since this electricity tariff is only available for a limited time window into the future we use least-squares support vector machines for electricity tariff price forecasting and thus provide the MPC controller with the necessary estimated time-varying costs for the whole prediction horizon. In the given context, the hourly pricing provides an economic incentive for a building controller to react sensitively with respect to high spot market electricity prices and high grid loading, respectively. Within the proposed tariff regime, grid-friendly behaviour is rewarded. It can be shown that peak electricity demand of buildings can be significantly reduced. The here presented study is an example for the successful implementation of demand response (DR) in the field of building climate control.
Conference Paper
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One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.
Conference Paper
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A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.
Conference Paper
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A preliminary study on the control of thermal energy storage in building cooling systems is presented. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated each night to recharge the storage tank in order to meet the buildings demand on the following day. A Model Predictive Control (MPC) for the chillers operation is designed in order to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This paper addresses real-time implementation and feasibility issues of the MPC scheme by using a (1) simplified hybrid model of the system, (2) periodic robust invariant sets as terminal constraints and (3) a moving window blocking strategy.
Article
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This paper presents a predictive control structure for thermal regulation in buildings. The proposed method exploits the intermittently operating mode of almost all types of buildings. Usually the occupation profile can be known in advance and this fact will be used to reduce the energy consumption without decreasing the thermal comfort during the occupation. For that purpose, the predictive control strategy is first presented for a single zone building then extended to a multizone building example. Two opposite control strategies commonly exists: the decentralized control structure, which does not offer good performances especially when the thermal coupling among adjacent rooms is not negligible, and on the other hand, the centralized control for which the computational demand grows exponentially with the size of the system, being very expensive for large scale buildings. Our solution is based on a distributed approach which takes the advantages of both methods mentioned above. A distributed MPC algorithm with one information exchange per time step is proposed with good control performances and low computational requirements. Simulations and a comparison performance table end the article.
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Heating, ventilation, and air conditioning (HVAC) systems are an important target for efficiency improvements through new equipment and retrofitting because of their large energy footprint. One type of equipment that is common in homes and some offices is an electrical, single-stage heat pump air conditioner (AC). To study this setup, we have built the Berkeley Retrofitted and Inexpensive HVAC Testbed for Energy Efficiency (BRITE) platform. This platform allows us to actuate an AC unit that controls the room temperature of a computer laboratory on the Berkeley campus that is actively used by students, while sensors record room temperature and AC energy consumption. We build a mathematical model of the temperature dynamics of the room, and combining this model with statistical methods allows us to compute the heating load due to occupants and equipment using only a single temperature sensor. Next, we implement a control strategy that uses learning-based model-predictive control (MPC) to learn and compensate for the amount of heating due to occupancy as it varies throughout the day and year. Experiments on BRITE show that our techniques result in a 30%–70% reduction in energy consumption as compared to two-position control, while still maintaining a comfortable room temperature. The energy savings are due to our control scheme compensating for varying occupancy, while considering the transient and steady state electrical consumption of the AC. Our techniques can likely be generalized to other HVAC systems while still maintaining these energy saving features.
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More than 15 years after model predictive control (MPC) appeared in industry as an effective means to deal with multivariable constrained control problems, a theoretical basis for this technique has started to emerge. The issues of feasibility of the on-line optimization, stability and performance are largely understood for systems described by linear models. Much progress has been made on these issues for non-linear systems but for practical applications many questions remain, including the reliability and efficiency of the on-line computation scheme. To deal with model uncertainty ‘rigorously’ an involved dynamic programming problem must be solved. The approximation techniques proposed for this purpose are largely at a conceptual stage. Among the broader research needs the following areas are identified: multivariable system identification, performance monitoring and diagnostics, non-linear state estimation, and batch system control. Many practical problems like control objective prioritization and symptom-aided diagnosis can be integrated systematically and effectively into the MPC framework by expanding the problem formulation to include integer variables yielding a mixed-integer quadratic or linear program. Efficient techniques for solving these problems are becoming available.
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