Search

The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators' manual conduction and/or previous level 2 systems control. [ABSTRACT FROM AUTHOR]

Building predictive control has proven to achieve energy savings and higher comfort levels than classical rule-based controllers. The choice of the model complexity needed to be used in model-based optimal control is not trivial, and a wide variety of model types is implemented in the scientific literature. This paper shares practical aspects of implementing different control-oriented models for model predictive control in a building. A real thermally activated test building is used to compare the white-, grey-, and black-box modeling paradigms in prediction and control performance. The experimental results obtained in our particular case reveal that there is not a significant correlation between prediction and control performance and highlight the importance of modeling the heat emission system based on physics. It is also observed that most of the complexity of the physics-based model arises from the building envelope while this part of the building is the most sensitive to weather forecast uncertainty. Dataset: https://doi.org/10.17632/xzdy23nzvj.1 [ABSTRACT FROM AUTHOR]

Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially. [ABSTRACT FROM AUTHOR]

Energy efficiency is extremely important for electric vehicles to improve their driving range. As the motor output energy directly acts on the tire, the tire slip energy generated by tire excessive slip will reduce the effective utilization of motor output energy, resulting in the reduction of energy efficiency, especially in the case of low friction coefficient, or even vehicle instability in some changes, such as slalom change, etc. Therefore, this paper designs an integrated chassis control and verifies the energy utilization. First, based on model predictive control (MPC), an integrated chassis controller is proposed by explicitly incorporating both motor energy and tire slip energy in the main objective function. Second, the reference control actions of the torque distribution method optimized for the motor energy, which facilitates solution-search process of MPC. Then, a semi-empirical UniTire tire slip energy model is proposed for derivation of the tire dissipation energy. Acceleration and stability tests are conducted by four in-wheel independent motor-drive electric vehicles (4WIMD-EVs) on winter proving ground, respectively. The acceleration change results show that both tire dissipation energy and motor output energy are well suppressed to achieve energy-efficient, and the energy utilization ratio of tire dissipation energy to motor output energy is only 35% to achieve stable acceleration. The slalom change verifies vehicle stability, and the results show that the slip ratio of four wheels only reaches to 0.04, and the motor output energy of four wheels is effectively utilized to generate differential yaw moment. [ABSTRACT FROM AUTHOR]

Nowadays, in many industrial applications, energy management is recognized as an essential issue. Comprehensive understanding of exergetic perspectives can help save more resources. A unique exergy-based optimization approach in model predictive control (MPC) framework is introduced in this paper to scale back Total Destroyed Exergy (TDE) of the controlled process. The proposed MPC facilitates the capability to address both the process and energy constraints in a multiple-input multiple-output (MIMO) system. To this end, the new MPC cost function is presented to unravel an optimal control problem supported TDE reduction and acceptable control performance to improve energy conservation. The findings will be demonstrated through a case study of industrial alkylation of benzene process to assess the effectiveness of the proposed energy-saving approach, which meets control performance needs. [ABSTRACT FROM AUTHOR]

Forecasts of temperature in a "smart" building, i.e. one that is outfitted with sensors, are computed from data gathered by these sensors. Model predictive controllers can use accurate temperature forecasts to save energy by optimally using heating, ventilation and air conditioners while achieving comfort. We report on experiments from such a house. We select different sets of sensors, build a temperature model from each set, and compare the accuracy of these models. While a primary goal of this research area is to reduce energy consumption, in this paper, besides the cost of energy, we consider the cost of data collection and management. Our approach informs the selection of an optimal set of sensors for any model predictive controller to reduce overall costs, using any forecasting methodology. We use lasso regression with lagged observations, which compares favourably to previous methods using the same data. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) has been successfully applied to many transportation systems. For the control of overhead cranes, existing MPC approaches mainly focus on improving the regulation performance, such as tracking error or steady-state error. In this paper, energy efficiency as well as safety is newly considered in our proposed MPC approach. Based on the system model designed, the MPC approach is applied to minimize an objective function that is formulated as the integration of energy consumption and swing angle. In our approach, promising results in terms of low energy consumption and small swing angle can be found, while the solutions obtained can satisfy all practical constraints. Our test results indicate that the MPC approach can ensure stability and robustness of improving energy efficiency and safety. [ABSTRACT FROM AUTHOR]

Pressure servo control plays a crucial role in the majority of industrial applications that use compressed air as their power source. How to reduce the energy consumption of the pneumatic system while ensuring the high accuracy of pressure control remains a key problem to be solved. This paper proposes a Gaussian Process (GP) 1 model predictive controller (MPC) applied to a pressure servo control system based on high-speed on-off valves. The smallest quantity of collected data is used to create the GP model. The entire model, which includes some predictive data, is produced once the generated model has been optimized. The GP model is combined with the MPC to perform comparative experiments using an industrial controller with a Field Programmable Gate Array (FPGA). The system responds quickly and with little tracking error in steady-state response studies as well as dynamic response experiments. The GP-MPC achieves the best outcomes in the dynamic comparison tests. The root mean square error (RMSE) is just 2.42 kPa, and the overshoot is less than 59.9% of the Proportional-Integral-Derivative (PID) controller and 68.5% of the sliding mode controller (SMC). Although the compressed air consumption of GP-MPC is basically the same as that of PID, it is significantly better than SMC, with a total saving of about 70.2%. All the experiments prove that the controller proposed in this paper can effectively improve the energy efficiency and tracking accuracy of the pressure servo control system. [Display omitted] • A Gaussian process model for the high-speed on-off valve is developed which is able to predict uncollected data. • A model predictive controller is established by combining the Gaussian process model with rolling optimization method. • The pressure tracking and energy-saving performance are intended to be improved by using the objective function. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a decision making and motion planning system for lane change maneuvers considering energy efficiency. A novel decision making algorithm is proposed to check the desirability of performing the lane change. The algorithm is based on a utility function that consists of different performance criteria, including energy consumption. The execution of the decided maneuver involves a lower-level motion planning and control system for the longitudinal and lateral directions. For the longitudinal direction, an energy-efficient Model Predictive Controller (MPC) is designed, which considers the safety boundaries as well as other constraints, such as comfort, traffic laws, and physical limitations of the system. For the lateral direction, the desired trajectory is planned based on a parameterized sigmoid function. The lateral tracking is then realized by a PID controller. Finally, to evaluate the performance of the designed algorithms, a fuel consumption map of an internal combustion engine (ICE) is approximated by a second-order multivariate polynomial. Simulation results demonstrate the capability of the proposed algorithm to safely perform the lane change maneuver in different scenarios and for two vehicle models, including a simplified vehicle dynamic model and a high-fidelity IPG CarMaker model. [Display omitted] • A new lane change decision making is proposed considering energy consumption. • An integrated energy-efficient planning and control framework is designed using MPC. • The framework considers safety, comfort, energy consumption, and actuator limitation. • The fuel map of an internal combustion engine is fitted to second-order polynomial. • The obtained polynomial is used in the optimization problem of energy-efficient MPC. • Simulation study for a simple mathematical model and a high-fidelity CarMaker model. [ABSTRACT FROM AUTHOR]

This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

Quench hardening is the process of strengthening and hardening ferrous metals and alloys by heating the material to a specific temperature to form austenite (austenitization), followed by rapid cooling (quenching) in water, brine or oil to introduce a hardened phase called martensite. The material is then often tempered to increase toughness, as it may decrease from the quench hardening process. The austenitization process is highly energy-intensive and many of the industrial austenitization furnaces were built and equipped prior to the advent of advanced control strategies and thus use large, sub-optimal amounts of energy. The model computes the energy usage of the furnace and the part temperature profile as a function of time and position within the furnace under temperature feedback control. In this paper, the aforementioned model is used to simulate the furnace for a batch of forty parts under heuristic temperature set points suggested by the operators of the plant. A model predictive control (MPC) system is then developed and deployed to control the the part temperature at the furnace exit thereby preventing the parts from overheating. An energy efficiency gain of 5.3% was obtained under model predictive control compared to operation under heuristic temperature set points tracked by a regulatory control layer. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

The deployment of model-predictive control (MPC) for a building's energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of reducing energy consumption within high-performance computing data centers, especially for those with a large portion of "small size" jobs. Different from previous works, the efficiency of job scheduling and processing is made as the first priority. To reduce energy from servers while maintaining the processing efficiency of jobs, a new hysteresis computing resource-provisioning algorithm is proposed to adjust the total computing resource reactively. A dynamical thermal model is presented to reflect the relationship between the computational system and cooling system. The proposed model is used to formulate constrained optimal control problems to minimize the energy consumption of the cooling system. Then, a two-step solution is proposed. Firstly, a thermal-aware resource allocation optimizer is developed to decide where the resource should be increased or decreased. Secondly, an economic model predictive controller is designed to adjust the cooling temperature predictively along with the variation of the rack power. Performance of the proposed method is studied through simulations with real job trace. The results show that significant energy saving can be achieved with guaranteed service quality. • On the basis of scheduling and processing jobs efficiently, a control framework is proposed for HPC data centers to coordinate hysteresis resource provisioning, thermal-aware allocation and dynamic cooling management. • An economic model predictive control based technique for the modeling and managing the thermal environment. • Comparison of proposed method is made with existing techniques. The proposed methodology achieves significant energy saving, and low performance loss in service quality. [ABSTRACT FROM AUTHOR]

Buildings are known for being great consumers of energy and for operating under On/Off and linear control strategies in common commercial packages. In order to reduce their energy footprints and to improve thermal comfort, new methodologies are needed to obtain better input profiles. This paper presents a detailed study of the implementation of a nonlinear model predictive control (NMPC) approach for a heating, ventilation, and air conditioning (HVAC) system. The HVAC system is modeled by an index-1 differential-algebraic system of equations, obtained from rigorous material and energy balances. The proposed NMPC algorithm was implemented in GAMS and compared with another approach from the literature. Similarly, the objective function is defined using one or more of the following criteria: (1) tracking of temperature and relative humidity set points; (2) maximization of thermal comfort; (3) minimization of energy consumption. To better represent the situation in a real application, the simulations include model-plant mismatch in parameters such as air infiltration into the building, the coefficient of the thermal exchange between the building and the exterior, and occupancy level. Additionally, simulations with random measurement noise have been performed. The results have shown that the proposed approach, in which the model is not linearized at any step, is able to reduce the energy consumption and maintain the Predicted Mean Vote (PMV) close to the desired set point, while the computation burden is only increased by one second per iteration, which is negligible in comparison with a sampling time of 10 min. [ABSTRACT FROM AUTHOR]

Model predictive controllers use accurate temperature forecasts to save energy by optimally controlling heating, ventilation and air conditioning equipment while achieving comfort for occupants. In a “smart” building, i.e. one that is outfitted with sensors, temperature forecasts are computed from data gathered by these sensors. Recently, accurate temperature forecasts have been generated using relatively few observations from each sensor. However, long sensor histories are available in smart houses. In this paper we consider improving forecast accuracy by using up to 24 hours of quarter-hourly readings. In particular, we overcome forecast inaccuracy that arises from the “one standard error” heuristic (1SE) in lasso regression. When there are many historical observations, low variance in the error estimations can result in excessively high values for the lasso hyperparameter λ. We propose the midfel refinement of lasso regression, which adjusts λ based on the shape of the error curve, resulting in improved forecast accuracy. We illustrate its effect in a setting where lasso regression is used to select sensors based on forecast accuracy. In this setting, midfel lasso regression using many historical observations has two effects: its improves accuracy and uses fewer sensors. Thus it potentially reduces costs arising both from energy usage and from sensor installation. [ABSTRACT FROM AUTHOR]

Battery equalization is a critical technology in energy storage systems, so that each storage cell has equal state. In the application of electric vehicle, equalization circuits and algorithms have been widely studied for the purpose of prolonging driving time, but optimization of equalization efficiency is a difficult task in the battery equalization of electric vehicle. In this paper, an optimization model with a linear form is proposed to incorporate both energy loss and equalization time for an energy-bus equalizer. In the consideration of different working status of electric vehicle, i.e., charging, discharging, and driving, dynamic equalization has been investigated, and a model predictive control approach is proposed to cope with frequent change of working status. According to simulation and experimental results, it can be concluded that energy and time efficiency can be significantly improved during dynamic battery equalization, and that the proposed equalization system is easily implemented with competitive simplicity due to the linearized system model. [ABSTRACT FROM AUTHOR]

In the heating, ventilation, and air-conditioning (HVAC) system of electric vehicles (EVs), an electric heater is often used for a reheating process that warms up the chilled evaporator outlet air for thermal comfort before it is supplied to the cabin. The usage of the electric heater can significantly reduce the driving range of an EV. Therefore, optimal control of the HVAC system with consideration of the reheating process becomes essential for an increased driving range. In addition, considering the primary role of cabin climate control, it is desirable to intelligently consider the passengers' thermal comfort in climate control design. In this paper, thermal comfort-conscious eco-climate control (TCC-ECC) based on model predictive control (MPC) is proposed to enhance the energy efficiency of the HVAC operation while ensuring passengers' thermal comfort. The MPC design uses a reduced-order HVAC system model based on an ideal vapor-compression cycle. For the integration of thermal comfort into the MPC, a new approach is proposed to obtain an approximate solution of a predictive mean vote (PMV)-based thermal comfort model, which aims to balance computational efficiency and prediction accuracy. With the proposed TCC-ECC, a parametric study is conducted to analyze the impact of weighting factors on energy consumption and thermal comfort under two different thermal load conditions. Then, the performance of the tuned TCC-ECC is evaluated in comparison with a rule-based (RB) controller and the baseline eco-climate control (ECC) without considering thermal comfort. In the performance evaluation, the proposed TCC-ECC demonstrates that with the inclusion of thermal comfort it performs better in terms of energy efficiency and thermal comfort than manually adjusting a target cabin temperature depending on the environmental thermal load. The energy consumption of the proposed TCC-ECC is 22.5% and 35.24% less than that of the RB controller at an ambient temperature of 24 °C and 35 °C, respectively, and 14.5% and 18.5% less than the baseline ECC at the same conditions, respectively. • Eco friendly climate control in an EV should consider both cooling and reheating. • Reheat process in an EV may consume significant energy. • Approximated solution for a PMV-based modified thermal comfort model. • Better energy efficiency by including thermal comfort in MPC-based climate control. [ABSTRACT FROM AUTHOR]

In seagoing ships in waves, the torque and thrust of the propeller may vary with the submergence depth of the propeller. A large ship motion can cause ventilation and loss of effective disc area, which degrade the ship's propulsion efficiency. Electric or electrified ships powered by electric motors can respond quickly to such load variations, unlike conventional ships powered by internal combustion engines. This paper proposes a model predictive anti-spin thruster control algorithm that can improve the propulsion efficiency of electric ships by controlling the rotational speed of the propeller with consideration of time-varying load conditions. The weight of the cost function for optimizing the propulsion efficiency is adjusted by using the propeller's submergence predicted by an autoregressive model. The feasibility of the proposed algorithm is shown through numerical simulations of ship motions and propeller depth variation in irregular waves. The performance of the proposed algorithm is validated and compared with that of shaft speed and anti-spin thruster controllers, and the results are discussed. [ABSTRACT FROM AUTHOR]

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach.

Belt conveyors and crushers are always assembled in series to form coal conveying systems; reasonably, this paper takes them as a whole for energy efficiency optimization. The energy models of the key energy consuming devices, belt conveyors and crushers, are firstly constructed. They are then employed to formulate an open loop energy efficiency optimization problem for the studied coal conveying systems. The coal feed rate, belt speed and crusher rotational speed are taken as the optimization variables; and, the energy cost, with consideration of time-of-use (TOU) tariff, is formulated as the objective function. Next, basing on the above open loop optimization problem, a closed-loop model predictive control (MPC) strategy is constructed. The MPC strategy has the ability to deal with various disturbances with its feedback correction and receding horizon optimization mechanisms. A coal conveying system in a coal-fired power plant is taken as a case study for verification of the two strategies. The open loop optimization and MPC strategies are investigated respectively for comparison studies. The results show that, unlike the open loop optimization, the MPC strategy can deal with the disturbances of coal consumption forecasting, the disturbances of belt feeding rate and the disturbances of mean particle size of feeding coal effectively. The MPC strategy can considerably improve the energy efficiency of the whole coal conveying system while satisfying all the constraints. Its robustness and adaptability are verified through the comparison studies. [ABSTRACT FROM AUTHOR]

Generally, conventional controllers for comfort are designed by using on/off control or proportional-integral (PI) control, with little consideration of energy consumption of the system. This paper presents a multi-input-multi-output (MIMO) model predictive control (MPC) for a direct expansion (DX) air conditioning (A/C) system to improve both indoor thermal comfort and air quality, whereas the energy consumption is minimised. The DX A/C system is modelled into a nonlinear system, with a varying speed of compressor and varying speed of supply fan and volume flow rate of supply air being regarded as inputs. We first propose an open loop controller based on an optimisation of energy consumption with the advantage of a unique set of steady states. The MPC controller is proposed to optimise the transient processes reaching the steady state. To facilitate the MPC design, the nonlinear model is linearised around its steady state. MPC is designed for the linearised model. The advantages of the proposed energy-optimised open loop controller and the closed-loop regulation of the MIMO MPC scheme are verified by simulation results. [ABSTRACT FROM AUTHOR]

Abstract: Forecasts of temperature in a “smart” building, i.e. one that is outfitted with sensors, are computed from data gathered by these sensors. Model predictive controllers can use accurate temperature forecasts to save energy by optimally using Heating, Ventilation and Air Conditioners while achieving comfort. We report on experiments from such a house, in which we select different sets of sensors, build a temperature model from each set, and then compare the accuracy of these models. While a primary goal of this research area is to reduce costs by reducing energy consumption, in this paper, besides the cost of energy, we consider the cost of data collection and management. Each sensor employed in the forecast calculation incurs costs for installation and maintenance and an incremental cost for computation. Some sensors, however, may contribute little or no improvement to the forecast accuracy. We incrementally construct sets of sensors until we arrive at a set for which no superset produces a better forecast. Then we construct a successive series of subsets, such that forecast accuracy degrades slowly. As each sensor is removed, on the one hand, the forecast error increases, so the energy costs may increase for a given controller. On the other hand, the costs for installing sensors and for computing models are reduced. By considering this tradeoff over the the series of sets, an optimal set of sensors can be found to be used with that controller. [ABSTRACT FROM AUTHOR]

This paper presents a smart building energy management system (BEMS), which is in charge of optimally controlling the sustainable operation of a building-integrated-microgrid (BIM). The main objective is to develop an advanced high-level centralized control approach-based model predictive control (MPC) considering variations of renewable sources and loads. A finite-horizon planning optimization problem is developed to control the operation of the BIM. The model can be implemented as a BEMS for the BIM to manipulate the indoor temperature and optimize the operation of the system's units. A centralized MPC-based algorithm is implemented for the power management scheduling of all sub-systems as well as power exchanges with the electrical grid. The MPC algorithm is verified over case studies applied to two floors residential building considering the climate condition of a typical day of March, where the effects of both loads and thermal resistance of building shell on the operation of the BIM are analyzed via numerical simulations. The analysis shows that 96% of the total electrical load has been fulfilled by the local production where 23% represents the total electric output of the micro-CHP and 73% is the renewable energy production. The deficit, which represents only 4%, is purchased from the electrical distribution network (EDN). [ABSTRACT FROM AUTHOR]

This paper focuses on the design and implementation of an event-based control architecture to manage a renewable-based microgrid. This microgrid has renewable-energy generation and a hybrid energy storage system that uses electricity and hydrogen. The main load of the microgrid is the energy demand of an office. The primary control objective is to satisfy this load using the available renewable generation and stored energy while reducing the amount of energy purchased from the Utility Power Grid and the degradation of the electromechanical storage devices. To do that, the control architecture defined within an event framework, makes use of a set of state-space model predictive controllers which are selected as a function of a variable sampling period. To evaluate the performance of the proposed architecture, simulation tests for a summer day as well as an analytical study is performed. The obtained results show that the use of the event-based control architecture allows a significant reduction of the number of changes in the control action at the expense of an acceptable deterioration of set-point tracking for a microgrid with several types of electrochemical storage. • This paper presents an Event-based State-Space Model Predictive Control system. • Two different types of events have been defined: threshold and time-based events. • It has been applied to efficiently manage a renewable-energy based microgrid. • The power supported by this microgrid is the one demanded by an office room. • The obtained results have shown a reduction in the number of actuations of 65%. [ABSTRACT FROM AUTHOR]

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to individual preferences of inhabitants and enables demand side management. While former mainly improves thermal comfort of inhabitants so does latter unlock ecological and financial opportunities mostly for energy utilities. Commonly, dynamic indoor conditioning is based on piece-wise constant indoor temperature constraints. This paper's contribution is the presentation of additional constraints: in particular ones expressed relative to the nominal behavior of a hydronic heating system. This allows to simultaneously harness the relevant process variables in particular during the pre-loading and post-loading phases of a load reduction. The findings are based on data sets acquired on 10 inhabited, residential buildings in Stockholm over a whole year. One of the findings is that building models need to be adaptable if predictive control is applied in practice. This adaptability is assured by a novel concept, i.e. a so called model manager on which the control is relying for the selection of the most accurate model. Centralized optimal control of buildings connected to a district heating network is challenging in practice due to a high computational load. In order to reduce it, the herein presented method elaborates plans only every hour instead of at every control step for optimal control. Since these plans cannot be optimal due to the lack of regular update a hitherto unknown cascaded control logic has been developed that corrects planning errors and other disturbances. Capabilities are demonstrated and compared to conventional controllers in dynamic simulations of a multi-zoned building. The herein presented method is to our knowledge the first to provide all flexibility desired by energy utilities and inhabitants alike through harnessing the consequences of transitions. [ABSTRACT FROM AUTHOR]

Buildings are responsible for almost half of the world's energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves. [ABSTRACT FROM AUTHOR]

This work focuses on evaluating the potential of variable speed limits (VSLs) in a synthetic urban network to improve both environmental sustainability and traffic performance. The traffic system is modeled using the microscopic traffic simulator SUMO, and a physical fuel consumption and NOx emission model is used to assess the vehicles’ energy efficiency. Speed limits are controlled through a nonlinear model predictive control (NMPC) approach, in which the traffic evolution and fuel consumption are respectively predicted with a macroscopic traffic model, namely the cell transmission model (CTM), and a pre-calibrated artificial neural network (ANN). The results reveal that in transient phases between different levels of congestion, the proposed eco-VSL controller is faster to decongest the network, resulting in an improvement of the environmental sustainability and the traffic performance both in the controlled network, and at its boundary roads. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the performance of Model Predictive Control (MPC) for a typical commercial building heating, ventilation and air conditioning (HVAC) system across a wide range of climate and weather conditions. The motivation of the study comes from the fact that although there is a large body of work on MPC for HVAC systems, there is a lack of studies that examine the range of possible performance of MPC, in terms of both energy savings and maintaining indoor climate (temperature and humidity) as a function of outdoor weather. A challenge in conducting such a study is developing an MPC controller that can be used in a wide range of weather. The root cause of this challenge is the need for a tractable cooling and dehumidification coil model that can be used by the MPC controller, since the coil may operate in quite distinct modes depending on weather. We present such an MPC controller, and then leverage it to conduct an extensive simulation campaign for fourteen climate zones in the United States and four weather conditions (winter, spring, summer, and fall) in each climate zone. The performance of the proposed controller is compared with not only a rule-based baseline controller but also with a simpler MPC controller that ignores humidity and latent heat considerations. There are several results the arise from this comparative study. One such result is that energy savings from MPC over baseline can vary dramatically based on climate and season. Another is that the effect of ignoring humidity in the MPC formulation can lead to poor indoor humidity control more in milder weather rather than in hot weather. The results from this study can help practitioners and researchers assess costs and benefits of proposed MPC formulations for HVAC control. • Performance of two MPC controllers for HVAC systems is studied. • The study spans a wide range of climate and weather conditions, a first of its kind. • MPC controller can perform poorly unless latent heat is considered in design. • Poor performance occurs typically in moist and marine climate zones. [ABSTRACT FROM AUTHOR]

Advanced optimization-based control, such as Model Predictive Control (MPC), has been shown to achieve increased energy savings and thermal comfort across different building types and climatic conditions. However, the success of such control algorithms is typically contingent on factors, such as sensor measurements used for the advanced control implementation. In this paper, we specifically investigate the role of occupancy sensors in improving the performance of MPC for a large office building situated in Chicago, IL weather conditions with VAV type HVAC system. Utilizing a detailed simulation-based study involving Occupancy-Based Model Predictive Control (OB-MPC), we infer that occupancy sensing could enable higher energy savings (∼ 5% on summer days) and improve thermal comfort relative to a baseline MPC that does not utilize occupancy information. Additionally, we investigate the impact of using occupancy presence sensors versus counting sensors, different levels of occupant density, and varying weather conditions on the control performance. Finally, we perform a systematic investigation of the impact of sensor non-idealities (specifically bias and latency errors) on the performance of OB-MPC algorithms. The study indicates that measurement bias could slightly degrade the realizable benefits of using occupant counting-based MPC (up to ∼ 1% reduction in energy savings and up to 2X increase in thermal discomfort compared to ideal sensing). However, measurement latency may not impact the control performance. [ABSTRACT FROM AUTHOR]

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants' numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality. [ABSTRACT FROM AUTHOR]

In this paper, a novel enhanced-efficiency selection of operating variables based on self-optimizing control (SOC) method for the vapor compression refrigeration cycle (VCC) system is proposed. An objective function is proposed to maximize the energy efficiency of the VCC system while meeting with the demand of indoor thermal comfort. With the detailed analysis of operating variables, three unconstrained degrees of freedom are selected among all the candidate operating variables. Then two SOC methods are applied to determine the optimal individual controlled variables (CVs) and measurement combinations as CVs. The model predictive control (MPC) method based controllers and PID controllers are designed for different sets of CVs, and the experimental results indicate that the proposed selection of CVs can achieve a good trade-off between optimal (or near optimal) stable operation and enhanced-efficiency of the synthesized control structure. [ABSTRACT FROM AUTHOR]

Predictive control technology for heating, ventilation and air conditioning (HVAC) systems has been proven to be an effective way to reduce energy consumption and improve thermal comfort within buildings. Such methods rely on models to accurately predict the thermal dynamics of a specific building to achieve the optimal control. Implementing a predictive control at the building level faces several challenges, since buildings’ thermal dynamics are nonlinear, time-varying, and contain several uncertainties. This paper presents a hybrid model predictive control (HMPC) scheme, which can minimise the energy and cost of running HVAC systems in commercial buildings. The proposed control framework combines a classical MPC with a neural network feedback linearisation method. The control model for the HMPC is developed using a simplified physical model, while the nonlinearity associated with HVAC process is handled independently by an inverse neural network model. To achieve the maximum energy saving, the proposed MPC integrates several advanced air-conditioning control strategies, such as an economizer control, an optimal start-stop control, and a load shifting control. This approach has been tested at the check-in hall of the T-1 building of the Adelaide Airport, through simulations and a field experiment. The merits of the proposed method compared to the existing control method are analysed from both the energy saving and cost saving points of view. The result shows that the proposed HMPC scheme performs reasonably well, and achieves a considerable amount of savings without violating thermal comfort. [ABSTRACT FROM AUTHOR]

This paper discusses the maintenance plan optimization problem for the energy efficiency purpose in the building energy efficiency retrofitting context. A subproblem namely the Building Retrofitted Facilities Corrective Maintenance Planning (BRFCMP) problem is proposed, where the corrective maintenance for malfunctioning retrofitted items are involved. The aggregate performances of the homogeneous retrofitted item groups, instead of the individual item performances, are the main consideration of the optimization issue. An aggregate population level optimization model is proposed to address the BRFCMP problem. When further taking into account the uncertainties, the optimization problem is cast into an optimal control problem to reduce the consequent adverse impact, given the dynamic nature of the aggregate performances of the item groups during operation. Both the optimization and control system approaches are applied to solve the BRFCMP problem without or considering uncertainties. An actual building retrofitting project is used as the case study to investigate the important role of maintenance to the building energy efficiency. The effectiveness of the proposed approaches is verified by simulation results. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is an advanced control that can be used for dynamic optimization of HVAC equipment. Although the benefits of this technology have been shown in numerous research papers, currently there is no commercially or publicly available software that allows the analysis of building systems that employ MPC. The lack of detailed and robust tools is preventing more accurate analysis of this technology and the identification of factors that influence its energy saving potential. The modeling environment (ME) presented here is a simulation tool for buildings that employ MPC. It enables a systematic study of primary factors influencing dynamic controls and the savings potential for a given building. The ME is highly modular to enable easy future expansion, and sufficiently fast and robust for implementation in a real building. It uses two commercially available computer programs, with no need for source code modifications or complex connections between programs. A simplified building model is used during the optimization, whereas a more complex building model is used after the optimization. It is shown that a simplified building model can adequately replace a more complex model, resulting in significantly shorter computational times for optimization than those found in the literature. [ABSTRACT FROM AUTHOR]

The paper proposes a new methodology based on integration of an exergy-auditing analysis with a model predictive control (MPC) scheme. The primary advantage of the methodology is that exergy analysis provides rich information to recognize processes that consume the major portion of energy. The proposed exergy-based MPC method thus is capable of incorporating the desired set-point values in conjunction with the corresponding operational constraints, being dictated by the exergy analysis, in design procedure of MPC. The proposed control scheme guides the system toward the desired set-points in a multi-input, multi-output (MIMO) configuration enhancement of the energy efficiency and prevention of loss of energy due to possible disturbances. The proposed method is comparatively implemented on an air separation unit as the benchmark case study in which a compressor – which is very common in gas industry – is identified as the main part with the major wasting energy. Variety of experiments was conducted to check the compressor under different disturbances. By 22 percent energy saving in the steady-state condition, the results clearly demonstrated superiority of the proposed methodology compared to the other energy-based MPC approaches. [ABSTRACT FROM AUTHOR]

The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency.

In this article, we investigate coordinated receding-horizon control of vehicle speed and transmission gearshift for energy-efficient operation of automated battery electric vehicles (BEVs). The introduction of multispeed transmissions in BEVs enables manipulation of electric motor operating point under given vehicle speed and acceleration command, thus creating the opportunity to further improve BEV energy efficiency. However, co-optimizing vehicle speed and transmission gearshift leads to a mixed-integer nonlinear programming (MINLP) problem, and it is well known that solving MINLP problems is computationally very challenging. To address this challenge, we propose a novel continuous relaxation technique that enables the computation of solutions to the speed and gearshift co-optimization problem using off-the-shelf nonlinear programming solvers. After analyzing theoretical properties of the proposed relaxation technique, we demonstrate its effectiveness through simulation-based case studies, where we show that co-optimizing vehicle speed and transmission gearshift can lead to considerably greater energy efficiency than optimizing them separately or sequentially and the proposed relaxation technique can reduce the computational cost of the co-optimization problem to a level that is comparable to the time budget available for onboard implementation. [ABSTRACT FROM AUTHOR]

This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers' different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi -level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation. • A real-time PCC system was developed to minimize the energy consumption of EVs. • Two scenarios of car-following and signal anticipation were evaluated. • An ANN model was developed to predict the energy economy performance of the EV. • The proposed PCC can realize 8.5%–15.6% of the energy saving in the urban areas. [ABSTRACT FROM AUTHOR]

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators’ manual conduction and/or previous level 2 systems control.