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Model predictive HVAC load control in buildings using real-time electricity pricing
... The number of samples for MPC controllers (RS, CEM, and MPPI) is 1000. The control cycle (timestep) is 15 minutes that is widely used in classic HVAC control [44]. We achieve convergence by 4.75x10 4 time-steps as explained in Section V-C1. ...
... The timestep for HVAC control is 15 minutes. The reason is that 15-minute control cycle is widely used in classic building control [44], [47]. There may have a benefit in doing finergrained control. ...
... As stated in the manual of EnergyPlus, the building dynamics models may be more accurate for shorter timesteps (10 minutes or less). However, based on the experience of classic control, the length of a control period should be no less than 10-15 min, because switching more frequently than once every 10-15 min can physically damage the normal HVAC equipment, like a heat pump [44]. ...
In this paper, we conduct a set of experiments to analyze the limitations of current MBRL-based HVAC control methods, in terms of model uncertainty and controller effectiveness. Using the lessons learned, we develop MB2C, a novel MBRL-based HVAC control system that can achieve high control performance with excellent sample efficiency. MB2C learns the building dynamics by employing an ensemble of environment-conditioned neural networks. It then applies a new control method, Model Predictive Path Integral (MPPI), for HVAC control. It produces candidate action sequences by using an importance sampling weighted algorithm that scales better to high state and action dimensions of multi-zone buildings. We evaluate MB2C using EnergyPlus simulations in a five-zone office building. The results show that MB2C can achieve 8.23% more energy savings compared to the state-of-the-art MBRL solution while maintaining similar thermal comfort. MB2C can reduce the training data set by an order of magnitude (10.52x) while achieving comparable performance to MFRL approaches.
... In Ref. [24], a multi input multi output (MIMO) robust controller is used for HVAC, which offers improvement in the system with disturbance rejection compared to a proportional plus integral (PI) controller. Model predictive control (MPC) has also gained popularity for HVAC applications [25][26][27]. A supervisory setpoint control optimizer based on MPC is coupled with a digital parameter-adaptive controller and used in a demand response system [6]. ...
... Finally, solving (25) for HVAC control gives: ...
In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors' and patients' desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control.
... These highly cited articles also demonstrated that MPC could be utilized to optimize indoor air quality (IAQ), such as the CO 2 concentration, by controlling the heating, ventilation, and air conditioning (HVAC) air supply (Wang & Jin, 2000;Mossolly et al., 2009;Kolokotsa et al., 2009). With the increasing penetration of renewable energy resources, researchers have applied MPC to improve building and renewable energy (e.g., photovoltaic) integration (Shakeri et al., 2017) and to minimize the energy cost during building operation (Chen et al., 2013;Avci et al., 2013). It is worthy to note that energy cost saving does not necessarily stem from building energy efficiency measures. ...
... It is worthy to note that energy cost saving does not necessarily stem from building energy efficiency measures. Although improving energy efficiency could help save energy cost, the energy cost saving could also be achieved through considering dynamic electricity pricing in the MPC formulation, as demonstrated in Chen et al. (2013) and Avci et al. (2013). In MPC operation, uncertainties (Maasoumy et al., 2014), optimal utilization of thermal mass (Privara et al., 2011;Ferreira et al., 2012), and occupancy (Dong & Lam, 2014) are all important dimensions to be considered. ...
Model predictive control (MPC) for smart building operation management has become an increasingly popular and important topic in the academic community. Based on a total of 202 journal articles extracted from Web of Science, this study adopted a science mapping approach to conduct a holistic review of the literature sample. Chronological trends, contributive journal sources, active scholars, influential documents, and frequent keywords of the literature sample were identified and analyzed using science mapping. Qualitative discussions were also conducted explore in details the objectives and data requirements of MPC implementation, different modeling approaches, common optimization methods, and associated model constraints. Three research gaps and future directions of MPC were presented: the selection and establishment of MPC central model, the capability and security of processing massive data, and the involvement of human factors. This study provides a big picture of existing research on MPC for smart building operations and presents findings that can serve as comprehensive guides for researchers and practitioners to connect current research with future trends.
... This is because detailed physics-based models usually entail long computation times, which are a major barrier to their online application in MPC [20]. The simplified physics models that are widely used in MPC include the resistive-capacitive model [12,21], heat balance model [10], discretetime state-space [22], and transfer function [23,24]. However, in actual application, these simplified models still require professional knowledge, as well as detailed information about, and parameters of, buildings and energy systems, which are sometimes difficult to obtain. ...
... According to the momentum balance, the velocity pressure that pumps provide can overcome the resistance of the valves, chillers, heat ex-changers, and cooling coils, as defined in Eqs. (22) to (23): ...
Renewable energy usage is continuing to increase as many countries worldwide are aiming to reach peak carbon emission and achieve carbon neutrality in the near future. One inherent problem with renewable energy is that its generation profile does not often fit well with the electricity usage profile. Therefore, it is of utmost importance that terminal users help to adjust the usage profile. Thermal energy storage (TES) systems have become an important means of adjusting the electricity usage profile of buildings. The operation strategy for TES must be carefully optimized to maximize its economic profile. To this end, we developed a framework for TES operation strategy optimization by integrating deep learning and physics-based modeling. The deep learning model, an attention-based dual-gated recurrent unit (A-dGRU) network, can learn the cooling load change trends from historical data and achieve state-of-the-art performance in hourly cooling load prediction for the next day with a coefficient of variation of the root mean square error of 0.08. For the TES modeling, we took the nonlinear change in the ice-charging rate into consideration based on the heat-transfer model; this change has often been ignored in previous studies. The high prediction accuracy and reliability of the TES model guarantee that the optimal strategy can be achieved by the framework. Compared to the basic TES operation strategy, we confirmed that the optimal operation strategy can further increase the cost savings by 11.2% for the entire ice-cooling season. In summary, the framework proposed in this study performs well in reducing the operation cost of a cooling plant based on the current electricity price tariff. The framework is expected to help the grid fit the electricity generation and usage profile.
... comfort [7,8]. This assertion is substantiated through both simulation [9][10][11][12][13] and experimental studies [14][15][16][17]. A pivotal aspect of implementing advanced predictive control involves the development of a control-oriented dynamic model. ...
... They have used particle swarm optimization (PSO) for the pre-training and enhancement of the optimization in the proposed algorithm. A model predictive methodology for the HVAC load management considers the inside temperature for the calculation of the cost of electricity [72]. The method provides energy efficient control to the customers for the energy management as per their requirement and preferences of inside temperature using temperature set-point assignment (TSA) algorithm. ...
In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.
... Some studies used model predictive control (MPC) to consider both the electricity price signal and the customers' preference of indoor comfort in DR control. Avci et al. [27] developed a DR control strategy based on MPC, collectively minimizing the electricity cost and the deviation between the indoor air temperature and the occupant's preferred temperature. They proposed a temperature setpoint assignment (TSA) algorithm to select indoor air temperature setpoints according to electricity price ranges and the discomfort tolerance index of occupants. ...
Demand response (DR) enhances building energy flexibility, but its application in hybrid heating systems with dynamic pricings remains underexplored. This study applied DR via heating setpoint adjustments based on dynamic electricity and district heating (DH) prices to a building heated by a hybrid ground source heat pump (GSHP) system coupled to a DH network. A cost-effective control was implemented to optimize the usage of GSHP and DH with power limitations. Additionally, four DR control algorithms, including two single-price algorithms based on electricity and DH prices and two dual-price algorithms using minimum heating price and price signal summation methods, were tested for space heating under different marginal values. The impact of DR on ventilation heating was also evaluated. The results showed that applying the proposed DR algorithms to space heating improved electricity and DH flexibilities without compromising indoor comfort. A higher marginal value reduced the energy flexibility but increased cost savings. The dual price DR control algorithm using the price signal summation method achieved the highest cost savings. When combined with a cost-effective control strategy and power limitations, it reduced annual energy costs by up to 10.8%. However, applying the same DR to both space and ventilation heating reduced cost savings and significantly increased discomfort time.
... The model aims to shift the equipment during consuming higher energy to the off-peak hours, but the problem with this approach is that some appliances cannot be moved and need to be operated at any time. The indoor temperature has been used by Avci, et al. [214] to calculate the cost of the electricity bill and load management in the HVAC using the predictive method. ...
Electronic device progress has increased demand for smart homes with IoT-based appliances. The advances in smart grid technology enabled each instant of energy consumption to be monitored in smart buildings. The problem is more energy consumption than ordinary homes compared with smart and standard devices. The requirement for efficient resource management is also increasing. As a result, scientists and researchers aim to optimize energy consumption and provide a comfortable environment, particularly in smart cities and buildings. In the previous research on this topic, the methods proposed in the literature have used static user parameters that fail to keep the balance of energy consumption and comfort index. In contrast, the proposed model uses deep learning to predict the dynamic indoor temperature, humidity, illumination, and CO2. This thesis focuses on balancing energy consumption optimization and comfort index in smart homes. Four parameters have been considered: temperature, humidity, illumination, and CO2 for the comfort index (thermal, visual, and air quality). The optimization module used the enhanced bat and krill herd algorithms regarding objective function and dynamic bounds, providing improved energy consumption optimization compared to static bounds. In addition, the enhanced fuzzy logic rules have more input and output membership functions, as in Malaysian environmental conditions. Hence provide more options for selecting optimal power based on the error difference between environmental and optimized parameters.
The RMSE results have proved that the bat algorithm's model has achieved an acceptable range of energy optimization while maintaining the comfort index of single and multi-users compared to the traditional model using the krill herd algorithm. The results indicate that the forecast and automation of user parameters have improved overall system performance in operating the system, efficient utilization of energy resources, and improved comfort index. The model using the bat algorithm has achieved an average optimized comfort index of 0.80, 0.72, and 0.87 for groups 1, 2, and 3 of the single-user models. The multi-user model's minimum, maximum, and average scenarios comfort index was 0.76, 0.88, and 0.87. Overall, the comfort index remained close to 1 for both single and multi-user models.
The energy consumption was reduced in the one-month scenario for the single-user model using the bat and krill algorithms with total optimization of 22.886% and 45.256%. Similarly, groups 2 and 3 using the bat algorithm have noticed an optimization of 26.639% and 37.018%, while the krill herd has an optimization of 41.873% and 35.950%. For the multi-user model one-month minimum scenario, bat and krill herd algorithms have optimized energy consumption to 19.022% and 55.547%. In the maximum scenario, the optimization remained at 36.287% and 43.689%. Finally, the average scenario has optimized energy consumption of 23.697% and 38.211%. In optimizing energy consumption, group 3 and the maximum scenario remained better than the other scenarios.
... The reductions of PAR and consumption cost optimization are both possible in smart grids thanks to twoway communication. Numerous studies have concentrated on the cost and PAR reduction provided by DSM as a result of the development of a smart grid [5][6][7] . However, the entirety of this research has not encompassed the inclusion of electricity production and storage for subsequent usage. ...
This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO.
... Model Predictive Control (MPC) can enable programming the building operation based on future weather and occupant behaviour. Avci et al. [14] proposed an efficient MPC-based approach to air conditioning system participation in DSM under dynamic pricing. Hu et al. [15] investigated the application of MPC for buildings' heating system control based on weather, occupancy, and dynamic prices. ...
This paper presents predictive control strategies for all-electric school buildings in cold regions to activate energy flexibility based on changes in electricity prices. A fully electric school building near Montreal, Canada, is used as a case study. This study investigates three scenarios: 1) Reference case with a proportional–integral controller and flat rate electricity price, 2) Model predictive control with flat rate electricity price, and 3) Model predictive control with dynamic electricity price. These scenarios are modelled using the resistance-capacitance thermal networks model, and energy performance is determined and compared over a typical heating season. The proposed approach takes into account the physical parameters of the building, weather predictions, and thermal comfort constraints to maintain optimal energy consumption. A building energy flexibility index is used to quantify the building energy flexibility with a focus on peak demand reduction when the electricity prices are higher than usual. The results show that the MPC strategy can reduce peak power demand by up to 100% and minimize the cost of electricity during demand response events while maintaining acceptable comfort conditions.
... The actual measurement results using the optimal operation scheduling based on the long-term data obtained through the BEMS show that the effect of energy reduction was achieved by about 20%. In addition, Avci et al. [17] conducted a study on changing indoor setup temperatures by 0.25 • C increments according to variation in electricity rates and setting the temperature to 25 • C at off-peak rate times and 26 • C at peak rate times, rather than setting a single specific value for the indoor temperature during summer. The simulation analysis results show that energy usage and costs were reduced by 23.6 and 24%, respectively. ...
Many countries adopt a time-of-use (TOU) rate system, in which electricity rates vary by season and time of day, to reduce power usage during peak power consumption hours. South Korea offers a TOU rate plan that depends on the electricity usage of a building and its contracted power; in this plan, the electricity rate reaches up to 300% depending on the time of day. Hence, electrically powered variable refrigerant flow (VRF) systems are increasingly being installed in small- and medium-sized buildings requiring individual cooling and heating operations. This study aims to develop a new control algorithm to reduce electricity consumption and electricity rates for cooling and heating by VRF systems in university buildings adopting the TOU rate plan and apply it to actual buildings to verify the reduction effect. The proposed control algorithm primarily consists of a module that controls the refrigerant evaporation temperature (cooling) and high pressure (heating) according to the indoor heat load and a module that controls the indoor set temperature based on the hourly electricity rate. The developed algorithm was installed in the controller of a VRF system installed in an actual university building and the annual effect was verified using the method proposed by the International Performance Measurement and Verification Protocol. As a result, power consumption was reduced by 17.8% for heating and 4.0% for cooling due to the application of the control algorithm, and the electricity rates reduced by 19.2% and 7.3%, respectively.
... In South Korea, four most energy intensive days are used for the same purpose [8]. A unique method for the calculation of CBL is provided in [12]. CBL features were employed in this strategy to find better matches in context of power consumption in previous time periods. ...
Demand response (DR) modifies the pattern from a straightforward to an interactive one, allowing for the investigation of consumer participation in the power market and progress. The most important technique for evaluating the status of DR program implementation among those discussed in the earlier literature is consumer baseline load (CBL). Without using the DR program, CBL depicts the consumer consumption pattern that might emerge. There hasn’t been much study done on CBL computation for residential consumers, and most of it ignores the influence of weather on these estimates. This study describes a novel method for computing CBL for residential consumers in the context of a smart grid. The findings show how weather affects CBL estimations and consumer desire to upgrade home insulation. The proposed method is examined in four test cases. The day-wise load data for the first test case has been collected from the NO1 region of Nord Pool and the remaining three test case data have been collected from the Connecticut and New Hampshire region of New England ISO.
... In the model-based control algorithms, some of the parameters are predicted, and this results in a more reliable but complex control strategy. For example, model-based HVAC control algorithms to minimize total energy costs for end-users were studied by Avci et al. [11]. However, model-based approaches have limited practical adoption due to its predictive model complexity and memory footprint required for the online optimization. ...
The current energy crisis raised concern about the lack of electricity during the wintertime, especially that consumption should be cut at peak consumption hours. For the building owners, this is visible as rising electricity prices. Availability of near real-time data on energy performance is opening new opportunities to optimize energy flexibility capabilities of buildings. This paper presents a reinforcement learning (RL)-based method to control the heating for minimizing the heating electricity cost and shifting the electricity usage away from peak demand hours. Simulations are carried out with electrically heated single-family houses. The results indicate that with RL, in the case of varying electricity prices, it is possible to save money and keep the indoor thermal comfort at an appropriate level.
... Tank stratification and mixing values allow for the average tank temperature to vary without affecting the output temperature delivered to heating or DHW, minimising the impact of DR on the consumer. The variation of setpoint temperature of HWSTs would not affect the end user as is the case with setpoint variation of room temperatures, where thermal comfort has to be factored in [30]. Therefore, the control for HWSTs only needs to meet the energy required for heating and DHW, simplifying the model. ...
Demand-responsive control of electrically heated hot water storage tanks (HWSTs) is one solution, already present in the building stock, to stabilise volatile energy networks and markets. This has been put into sharp focus with the current energy crisis in Europe due to reduced access to natural gas. Furthermore, increasing proportions of intermittent renewable energy will likely add to this volatility. However, the adoption of demand response (DR) by consumers is highly dependent on the economic benefit. This study assesses the economic potential of DR of centralised HWSTs through both an analysis of spot price data and an optimisation algorithm approximating DR control. The methods are applied to a case study apartment building in Norway using current pricing models and examine the effect of the demand profile, electricity prices, heating power and storage capacity on energy cost and energy flexibility. Unit cost savings from DR are closely linked to the variation in unit energy price during the optimisation period. Increasing the storage capacity or the heating power increases the flexibility with a diminishing rate of return. However, increasing storage capacity does not result in cost savings as additional heat losses are greater than the saving from shifting demand, except for during highly volatile electricity price periods. Changing the minimum setpoint temperature improves the cost curve as a greater thermal storage capacity can be achieved without increasing heat loss. Systems utilising a smaller heating power are more economical due to the dominant role of the monthly price related to the peak energy demand of the system.
... The suggested method for pre-training and optimization augmentation utilises Particle Swarm Optimization (PSO). By using an HVAC load control model forecasting technique [24], the cost of energy is calculated while accounting for the indoor temperature. Using the temperature set-point assignment (TSA) technique, the system provides clients with energy-efficient control for energy monitoring depending on their demands and preferences for interior temperature. ...
... Two types of DR programs exist in practice: incentive-based DR programs, and price-based DR programs. In the first type of DR program, the utility company can shutdown consumers' load on short notice at the time of need [2,3]. In the second type of DR program, consumers are actively involved in load management in response to pricing signals. ...
Energy consumption schedulers have been widely adopted for energy management in smart microgrids. Energy management aims to alleviate energy expenses and peak-to-average ratio (PAR) without compromising user comfort. This work proposes an energy consumption scheduler using heuristic optimization algorithms: Binary Particle Swarm Optimization (BPSO), Wind Driven Optimization (WDO), Genetic Algorithm (GA), Differential Evolution (DE), and Enhanced DE (EDE). The energy consumption scheduler based on these algorithms under a price-based demand response program creates a schedule of home appliances. Based on the energy consumption behavior, appliances within the home are classified as interruptible, noninterruptible, and hybrid loads, considered as scenario-I, scenario-II, and scenario-III, respectively. The developed model based on optimization algorithms is the more appropriate solution to achieve the desired objectives. Simulation results show that the expense and PAR of schedule power usage in each scenario are less compared to the without-scheduling case.
... Model predictive control is gaining widespread attention as an advanced control strategy for residential heating systems, and heat pumps in particular, since it can systematically improve thermal comfort with simultaneous energy and/or cost savings, as well as enable the provision of services to the rest of the energy system (Serale et al. (2018); Drgoňa et al. (2020); ; Avci et al. (2013); Bianchini et al. (2016)). The performance of any MPC strategy is dependent on the accuracy of the mathematical model describing the thermal loads and the forecasts of disturbances, such as weather and user behavior. ...
... One is based on price, the other on incentives. In an incentive-based programme, the utility can send a brief message to the Energy Management Controller (EMC) of the Smart Home (SH) or Smart Building (SB) to a switch connected via wireless media that shows the status and toggles the switches upon detecting a peak from user's appliances [1][2][3][4]. Consequently, the utility can wirelessly lower the Peak to Average Ratio (PAR). Prices during off-peak hours encourage the user to utilize their appliances. ...
We are living in the age of electricity, and it has become an essential part of our lives. We generate power in power stations, which are then transmitted to users for usage. The power that is generated at power stations are fixed and can be increased only through the replacement of the whole structure and this is not a feasible solution to be repeated
multiple times, because of many issues that retain it from doing this. So in order to use the generated power efficiently, smart grids play a significant role in minimizing electricity consumption and cost through Demand Side Management (DSM). Smart homes, a specific portion of smart grid performs a very significant role in minimizing cost and consumption via scheduling home appliances. As a result, user waiting time will increase. This scheduling problem is viewed as an optimization problem in this work. An efficient scheme called the Sine-Cosine algorithm (SCA) is being employed in this work. The proposed
scheme is implemented on the campus load of a university. Which contains a variety of appliances that are scheduled using the SCA technique. The simulations produced extremely beneficial results in terms of Peak to Average Ratio (PAR) and electricity cost reductions, while keeping an appropriate threshold ratio between the cost of electricity i.e. electrical bill and waiting time of users i.e. user’s comfort.
... У [22] будується система реального часу, яка враховує поточну погодинну вартість за енергоносії та обчислює індекс дискомфорту в режимі реального часу. Ці дані є вхідними параметрами для методу побудови моделі з прогнозуванням, окрім поточного значення температури та вологості. ...
Overview of shopping malls ventilation and air conditioning energy-efficient system control needs
The article is devoted to the analysis of design-specific features and microclimate control problems searching of the shopping malls.
The main trends of shopping malls development are formulated. It is shown that Fagner indices and ISO 7730: 2005, ISO10551: 2019 standards are used to evaluate human comfort indoors. It has been established that improper usage of shopping malls ventilation can be a cause of visitors’ and employees’ illnesses. It was found that parameters that need to be controlled are not limited to typical like temperature and humidity. It is proposed to make measurements of the following parameters: carbon monoxide, PM2.5 and PM10 particles, carbon dioxide (CO2), formaldehyde (HCHO), volatile organic compounds (TVOC). It is shown that human comfort in the shopping mall creation is an important task that needs to be solved in conjunction with energy saving. The main parameters of the microclimate that should be used in system control synthesis are given. The main shopping malls devices of technological processes automation control manufacturers are presented. It is emphasized that
energy efficiency approaches are divided into constructive and lgorithmic. The constructive approaches implementation is possible only if the shopping center was built in compliance with all regulatory requirements of premises construction. A review of control laws and modeling methods that are used in eating and ventilation systems control is provided. The basic principles of energy resources saving are formulated. It is determined that shopping center is an object with distributed parameters due to its size and separate conditions for each room creation needs. It is shown that classical approaches that are proposed to be used for temperature and humidity processes of the object with distributed parameters control are not suitable for its energy efficiency.
Keywords: shopping center; energy saving; automated control system; microclimate; comfort index
... 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. ...
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 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. ...
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.
... 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. ...
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.
... 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. ...
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.
... 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). ...
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.
... 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. ...
In this study, we propose a modified model predictive control (MPC) strategy for managing the thermal load in buildings, aimed at creating a fine-tuned balance between indoor thermal comfort and electricity cost reduction. Here, the multi-zone building’s state-space model is employed to dynamically manage energy consumption while preserving occupant comfort. The key contributions of this work include the development of a novel economic MPC strategy tailored for multi-zone heating, ventilation, and air conditioning (HVAC) systems, integrating thermal energy storage to optimise energy usage and occupant comfort. Additionally, we introduce an enhanced multi-objective optimisation framework that transforms the conflicting objectives of energy efficiency and occupant comfort into a single-objective problem for improved computational efficiency. The control strategy also incorporates dynamic electricity pricing, enabling cost-effective operation by shifting energy consumption to lower-cost periods. The proposed control method reduces fluctuations in indoor air temperature, extending the operational life of HVAC system actuators. Beyond reducing costs and consumption, this approach alleviates energy production strain and peak demand on the smart grid. The optimisation process incorporates user-defined temperature preferences for each zone, ensuring tailored comfort conditions. Simulation results show that this method maintains indoor air temperature within the desired comfort range, outperforming traditional methods prone to fluctuations. Furthermore, the proposed MPC strategy effectively shifts the peak load to periods of lower electricity prices, achieving an 18.58% reduction in overall energy costs.
The evolution of Internet-of-Things (IoT) is fostering the use of intelligent controls for energy conservation. Yet, the efficacy of these strategies is largely tied to diverse load forecasting algorithms. Given the significant contribution of heating, ventilation, and air-conditioning (HVAC) systems to global energy consumption, accurate forecasting of HVAC power usage is crucial for improving overall energy efficiency. However, real-world HVAC load forecasting, bolstered by various IoT devices, is complicated by multiple factors: data variability, power load fluctuations, electronic phenomena (e.g., zero drifts), and the increased time complexity and larger model sizes required to manage accumulating historical data. To address these challenges, we first present an in-depth measurement study on the characteristics of HVAC load at a minute scale based on HVAC data collected in six locations. We propose HALO, a transformer-based framework specifically designed for forecasting HVAC load. HALO incorporates an adaptive data pre-processing stage and a local-global-scale transformer-based load forecasting stage, enabling precise forecasting of HVAC load and optimization of energy utilization. Evaluation based on real-world data traces from a prototype application demonstrates that the proposed framework significantly outperforms existing models.
The space conditioning of buildings is liable for more than 10% of World final energy uses and related CO2-eq emissions. Such share must be drastically reduced to pursue sustainability by optimizing both energy design and devices control. In this frame, space cooling is assuming an increasing weight owing to climate change. Accordingly, this study applies a simulation- and optimization-based framework for the model predictive control (MPC) of space cooling systems. The case study is a nearly zero energy building located in Benevento – Southern Italy, Mediterranean climate – featuring an efficient air-source multi-split system for cooling. The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. Accordingly, a Pareto multi-objective approach is applied considering different discomfort indicators to compare the Fanger theory with the adaptive one of ASHRAE 55. The optimization problem is solved by running a genetic algorithm – variant of NSGA II – under MATLAB® environment. The objective functions are assessed via the coupling between MATLAB® and EnergyPlus, using a validated building energy model. The multi-criteria decision-making is performed by setting a limit to discomfort to pick an optimal Pareto solution. The framework is tested addressing a typical day of the cooling season and using monitored weather data to simulate weather forecasts. Different optimal solutions are provided to fit different comfort categories. Compared to a reference control at fixed setpoint – 26 °C – the proposed solutions with similar comfort performance ensure cost savings around 28%. Besides the proposed hypothetical implementation, the framework can be integrated in automation systems for real-time MPC. The novel contributions of this study lie in the methodology to combine MPC with different thermal comfort models as well as in the results, which provide deeps insights about the application of MPC for the space cooling of nearly zero energy buildings in a balanced climate.
Several studies have indicated a significant potential in using Model Predictive Control (MPC) of space heating for demand response purposes. The performance of the MPC depends on the predictive performance of the embedded control model. The studies often employ black- or grey-box control models; however, no previous studies consider whether a black- or grey-box model is more robust against weather changes. To assess this, the simulation-based study reported in this paper analysed how the predictive performance of black- and grey-box models trained with different input-output datasets from a certain period of a year is affected when subject to weather conditions in other periods of the year. The predictive performance of the grey-box models was slightly better compared to the black-box model. Furthermore, the grey-box models were slightly more robust to changes in weather data. Future studies should investigate whether the differences have practical significance in relation to MPC.
Fusion's light steel frame system was chosen as a viable lightweight steel solution that would equal the pace of construction of a timber frame, eliminate fire hazards during and after construction, reduce waste, provide greater durability, versatility, safety, quicker construction timeframes, cost-effectiveness, and space maximisation, and minimise building shrinkage and movement for sustainability, thermal, reliable, and acoustic construction by simplifying construction with recycled materials, according to industry recommendations. This research provides information and recommendations for using light steel frames in residential construction, such as single-family homes and apartments. Galvanized cold-formed steel sections that may be fitted as prefabricated panels are the main structural elements of light steel framing systems. This research focuses on design elements that are governed by the building code (England and Wales). It also looks into the construction and characteristics of light steel frames, as well as how they interact with other materials and components.
Rapid growth in global energy consumption has raised concern on the environmental impacts such as ozone layer depletion and climate change. Enclosed space, such as commercial buildings, accounts for about 40% of global energy consumption and the demand is constantly increasing due to increasing population, urbanization, and economic development. The energy demands in the building sector calls for strategic measures to develop energy efficient technologies. This paper presents a strategy to decrease energy demands inside buildings by proposing a ventilation system which regulates the enclosed air quality resulting in reduced air conditioning. The system consists of multiple adsorption beds with zeolite 13X monoliths for CO2 removal, and silica gel for humidity control, inside the enclosed space. The air conditioning system results in decrease in energy requirement and improvement in economics by 55% as compared with conventional ventilation system. The model is scaled up to the size comparable with total office inventory of New York City, and the reduction in carbon emissions by introducing the air composition control system for New York City is equivalent to replacing 57 million incandescent light bulbs by LEDs. This paper concludes that the air conditioning system proposed in this study results in the improvement in performance as compared to a conventional ventilation system and could reduce energy consumption inside commercial buildings.
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.
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.
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.
This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate control under combined parametric and additive uncertainties. The proposed SMPCap approach enables the quantification, and manipulation, of both the mean and covariance of the stochastic system states and inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared to the conventional deterministic MPC (DMPC) and the state-of-the-art SMPCa only accounting for additive uncertainties, at the cost of a maximum relative increase in energy use of 21.6% and 4.2%, respectively. By incorporating the SMPCap strategy in an integrated optimal control and design (IOCD) approach, its additional added value for obtaining a more appropriate, yet robust, heat supply system sizing is illustrated. Via simulations, size reductions up to 33.3% are shown to be achievable for a terraced single-family dwelling without increasing thermal discomfort compared to an IOCD approach incorporating DMPC.
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.
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.
The quantification of heating, ventilation, and air condition (HVAC) system flexibility is critical to the operations of both the grid and buildings in demand response (DR) programs. However, the flexibility quantification is challenging due to the non-linearity and non-convexity of thermal dynamics associated with HVAC components. This paper proposes a novel HVAC flexibility quantification method based on a semidefinite programming (SDP) formulation. The SDP is reformulated from the non-convex problem of HVAC power optimization, and can be solved efficiently in real-time. The physics-based HVAC model is incorporated to ensure the reliability and accuracy of solutions. The quantification results are organized into an HVAC flexibility table that can provide response strategies on adjusting HVAC setpoints in response to the grid signals received. The developed response strategies minimize occupant discomfort while satisfying grid requirements. A case study of a test building model is carried out to illustrate the flexibility quantification framework and compares the performance of two DR strategies.
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.
Timely and effective deployment of demand response could greatly increase power system flexibility, electricity security and market efficiency. Considerable progress has been made in recent years to harness demand response. However, most of this potential remains to be developed. The paper draws from IEA experience to identify barriers to demand response, and possible enablers that can encourage more timely and effective demand response including cost reflective pricing, retail market reform, and improved load control and metering equipment. Governments have a key role to play in developing and implementing the policy, legal, regulatory and market frameworks needed to empower customer choice and accelerate the development and deployment of cost-effective demand response.
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.
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.
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.
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.
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.
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.
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.
This paper discusses the design of low-frequency pulse width modulation for heating ventilation and air conditioning (HVAC) compressors. HVAC units are traditionally controlled using nonlinear control techniques like hysteresis control. Using a very long pulse width, this method can treat an on/off air conditioner or heat pump compressor as a variable input for which traditional linear (or nonlinear) controls can be applied. The key advantage of this method is direct control over the compressor power using tunable saturation. Power control is especially useful when considering load management and real time energy pricing.
Low energy buildings have attracted lots of attention in recent years. Most of the research is focused on the building construction or alternative energy sources. In contrary, this paper presents a general methodology of minimizing energy consumption using current energy sources and minimal retrofitting, but instead making use of advanced control techniques. We focus on the analysis of energy savings that can be achieved in a building heating system by applying model predictive control (MPC) and using weather predictions. The basic formulation of MPC is described with emphasis on the building control application and tested in a two months experiment performed on a real building in Prague, Czech Republic.
The paper presents a model-based predictive control algorithm that uses a limited number of control sequences for on-line simulation of future behaviour of the process. Each control sequence used in simulation generates a predicted sequence of the output signal. The predicted output sequences are analysed and evaluated and then, using a set of rules, the `optimal' control signal is computed. To simulate the future behaviour of the process it is used a process model and also the previous sequences of the input and output signals from the process. The algorithm permits directly use of the nonlinear model of the process. The algorithm is used for simulation of the temperature control in a house and compared with the usual algorithms of type PI.
The present work introduces a mathematical model for a heat pump with a variable-speed compressor, driven by a d.c. servomotor, operating either in closed loop by a power law control action or by the traditional on-off basis. The resulting differential and algebraic equations are integrated in time for a specified period of simulation in both designs. The results show that the closed-loop system presents significant savings in energy consumption when compared with the on-off system, under the same environmental conditions.RésuméDans cet article, on présente un modèle mathématique pour une pompe à chaleur avec un compresseur à vitesse variable, qui est entrainé par un moteur à courant direct, et qui fonctionne soit en boucle fermée avec régulation de puissance ou par le traditionnel système d'action par tout ou rien. Les équations différentielles et algébriques obtenues sont intégrées dans le temps pour une période de simulation spécifiée dans les seux conceptions. Les résultats montrent que le système en boucle fermée permet d'importentes économies d'énergie par rapport à l'action par tout ou rien, dans les mêmes conditions environnementales.
The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with HVAC (heating, ventilation and air conditioning) systems. The occupants’ thermal comfort sensation is addressed here by the well-known comfort index known as PMV (predicted mean vote) and by a comfort zone defined in a psychrometric chart. In this context, different strategies for the control algorithms are proposed by using an only-one-actuator system that can be associated to a cooling and/or heating system. The first set of strategies is related to the thermal comfort optimization and the second one includes energy consumption minimization, while maintaining the indoor thermal comfort criterion at an adequate level. The methods are based on the model predictive control scheme and simulation results are presented for two case studies. The results validate the proposed methodology in terms of both thermal comfort and energy savings.
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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