615 research outputs found

    Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

    Get PDF
    In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted

    Dual estimation: Constructing building energy models from data sampled at low rate

    Get PDF
    AbstractEstimation of energy models from data is an important part of advanced fault detection and diagnosis tools for smart energy purposes. Estimated energy models can be used for a large variety of management and control tasks, spanning from model predictive building control to estimation of energy consumption and user behavior. In practical implementation, problems to be considered are the fact that some measurements of relevance are missing and must be estimated, and the fact that other measurements, collected at low sampling rate to save memory, make discretization of physics-based models critical. These problems make classical estimation tools inadequate and call for appropriate dual estimation schemes where states and parameters of a system are estimated simultaneously. In this work we develop dual estimation schemes based on Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF) for constructing building energy models from data: in order to cope with the low sampling rate of data (with sampling time 15min), an implicit discretization (Euler backward method) is adopted to discretize the continuous-time heat transfer dynamics. It is shown that explicit discretization methods like the Euler forward method, combined with 15min sampling time, are ineffective for building reliable energy models (the discrete-time dynamics do not match the continuous-time ones): even explicit methods of higher order like the Runge–Kutta method fail to provide a good approximation of the continuous-time dynamics which such large sampling time. Either smaller time steps or alternative discretization methods are required. We verify that the implicit Euler backward method provides good approximation of the continuous-time dynamics and can be easily implemented for our dual estimation purposes. The applicability of the proposed method in terms of estimation of both states and parameters is demonstrated via simulations and using historical data from a real-life building

    Model-predictive control for non-domestic buildings: a critical review and prospects

    Get PDF
    Model-predictive control (MPC) has recently excited a great deal of interest as a new control paradigm for non-domestic buildings. Since it is based on the notion of optimisation, MPC is, in principle, well-placed to deliver significant energy savings and reduction in carbon emissions compared to existing rule-based control systems. In this paper, we critically review the prospects for buildings MPC and, in particular, the central role of the predictive mathematical model that lies at its heart; our clear emphasis is on practical implementation rather than control-theoretic aspects, and covers the role of occupants as well as the form of the predictive model. The most appropriate structure for such a model is still an open question, which we discuss alongside the development of the initial model, and the process of updating the model during the building’s operational life. The importance of sensor placement is highlighted alongside the possibility of updating the model with occupants’ comfort perception. We conclude that there is an urgent need for research on the automated creation and updating of predictive models if MPC is to become an economically-viable control methodology for non-domestic buildings. Finally, more evidence through operating full scale buildings with MPC is required to demonstrate the viability of this method

    Methods For Data-Driven Model Predictive Control

    Get PDF
    Model predictive control (MPC) is essential to optimal decision making in a broad range of applications like building energy management and autonomous racing. MPC provides significant energy cost savings in building operations in the form of energy-efficient control with better occupant comfort, lower peak demand charges, and risk-free participation in demand response. In autonomous racing, MPC computes a safe minimum-time trajectory while driving at the limit of a vehicle’s handling capability. However, the ease in controller design depends upon the modeling complexity of the underlying physical system. For example, the identification of physics-based models of buildings is considered to be the biggest bottleneck in making MPC scalable to real buildings due to massive engineering effort. Thus, the traditional modeling approaches like the white-box and the grey-box techniques, although detailed, are considered cost and time prohibitive. In the case of autonomous racing, one of the fundamental challenges lies in predicting the vehicle’s future states like position, orientation, and speed with high accuracy because it is inevitably hard to identify vehicle model parameters that capture its real nonlinear dynamics in the presence of lateral tire slip. To this end, we present methods for data-driven MPC that combine predictive control and tools from machine learning such as Gaussian processes, neural networks, and random forests to reduce the cost of model identification and controller design in these applications. First, we introduce learning and control algorithms for building energy management based on black-box modeling that require minimum external intervention and solve some of the fundamental practical challenges ranging from experiment design to predictive control to online model update. We learn dynamical models of energy consumption and zone temperatures with high accuracy, and demonstrate load curtailment during demand response, energy savings during regular operations, and better occupant comfort compared to the default system controller. We validate our methods on several buildings in different case studies, including a real house in Italy. Next, we present a model-based planning and control framework for autonomous racing based on discrepancy error modeling that significantly reduces the effort required in system identification of the vehicle model. We start with an easy-to-tune but inaccurate physics-based model of the vehicle dynamics and thereafter correct the model predictions by learning from prior experience. Our approach bridges the gap between the design in a simulation and the real world by learning from on-board sensor measurements. We demonstrate its efficacy on a 1/43 scale autonomous racing simulation platform

    INDOOR ENVIRONMENTAL QUALITY (IEQ) AND BUILDING ENERGY OPTIMIZATION THROUGH MODEL PREDICTIVE CONTROL (MPC)

    Get PDF
    This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings’ thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These include thermal comfort control based on ASHRAE comfort zone (based on temperature and relative humidity) and Predicted Mean Vote (PMV) and ventilation control based on ASHRAE 62.1 and Demand Control Ventilation (DCV). The building energy consumption was also evaluated with and without integrated lighting and window blind control. The simulation results revealed better performance of MPC both in terms of energy savings as well as maintaining acceptable indoor environmental quality. Energy saving as high as 48% was possible using MPC with integrated lighting and window blind control. A new critical contaminant - based demand control ventilation strategy was also developed to ensure acceptable or higher indoor air quality. Common indoor and outdoor contaminants were considered in the study and the method resulted in superior performance especially for buildings with strong indoor or outdoor contaminant sources compared to conventional CO2 - based demand control ventilation which only monitors CO2 to vary the minimum outdoor air ventilation rate

    Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

    Get PDF
    The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand
    • …
    corecore