Energy Management in Buildings: Lessons Learnt for Modeling and Advanced Control Design

This paper presents a comparative analysis of different modeling and control techniques that can be used to tackle the energy efficiency and management problems in buildings. Multiple resources are considered, from generation to storage, distribution and delivery. In particular, it is shown what are the real needs and advantages of adopting different techniques, based on different applications, type of buildings, boundary conditions. This contribution is based widely on the experience performed by the authors in the recent years in dealing with existing residential, commercial and tertiary filed buildings, with application ranging from local temperature control up to smart grids where buildings are seen as an active node of the grid thanks to their ability to shape the thermal and electrical profile in real time. As for control models, a wide range of modeling techniques are here investigated and compared, from linear time-invariant models, to time-varying, to nonlinear ones. Similarly, control techniques include adaptive ones and real-time predictive ones

Hierarchical Nonlinear MPC for Large Buildings HVAC Optimization

This paper studies the problem of performance improvement and energy consumption reduction of the heating, ventilation and air conditioning system of a large-scale university building through the application of nonlinear predictive control strategies concerning also practical and implementation issues. The system consists of two heat pumps, a water-to-water and an air-to-water type, and two different air handling units, which regulate and circulate air in all thermal zones. In such applications, prediction of the future dynamical behavior of the heat pumps is extremely important to enforce efficiency, but it is also very challenging due to the load dependency and nonlinearity of the coefficient of performances of those heat pumps. On the other hand, another source of potential model mismatch is the nonlinear characterization of the heat transfer coefficients of the AHU induced by variable air and water velocity, which gives rise to a non-trivial nonlinear system. To do so, two nonlinear model predictive control strategies are investigated to deal with many physical constraints and nonlinear problems. Finally, a sensitivity and robustness analysis are performed to highlight the merits, defects and impacts of those control algorithms on the energy performance of the building

Neural network predictive schemes for building temperature control: a comparative study

Starting from an application of a real medium-size university building, the present paper focuses on the comparison among different ways to synthesize a predictive control scheme to improve the energy performance for heating, ventilation and air conditioning system of the building. The main motivation is the comparison among a nonlinear predictive control structure previously developed (based on first principle equations) with a predictive control whose prediction model is an artificial neural network. Particular emphasis is given on how to tune the neural network to gain good closed-loop performance. Twenty-one different networks are designed and tuned in order to correlate their closed-loop performance with the type and length of training data set, for building energy efficiency applications. Finally, a linear time-variant predictive control is given, obtained as analytical linearization along the future system trajectory, of the nonlinear equations of the neural network model. The goal is to add to the comparison a low computational burden (linear controller) still derived from nonlinear data-driven methods

Predictive control-oriented models of a domestic air-To-water heat pump under variable conditions

Air-To-water heat pumps are quite often integrated with a hot-water tank, to better decouple the generation from the delivery of heat in buildings and to improve the overall performance of the system. The estimation and prediction of the coefficient of performance of the heat pump is extremely important to enforce efficiency, but it is also a very challenging task, due to the strong dependency of the performance on disturbances and operating conditions. Another source of potential model mismatch is the variable water flow rate in the condenser side induced by the heat pump low-level control logic, which gives rise to a non-Trivial nonlinear system. In this letter, we tackle the problem to develop and properly tune an equivalent control-oriented model for the system, i.e. heat pump and tank, under variable flow rate conditions on both the condenser and load side, while still preserving good prediction capabilities of the model, with no tank temperature nor mass flow rate sensors. In particular, we focus on a real case study consisting in an air-To-water heat pump system and a 150{m2} building located in SYSLAB, Department for Electrical Engineering, Risoe Campus, Denmark Technical University. The quality of the developed models is then evaluated through a nonlinear model predictive controller suitably designed and checked against detailed reference models previously developed. Finally, different sensitivity analyses are performed, which witness the robustness of the proposed algorithm

A Distributed Predictive Control of Energy Resources in Radiant Floor Buildings

This paper studies the impact of using different types of energy storages integrated with a heat pump for energy efficiency in radiant-floor buildings. In particular, the performance of the building energy resources management system is improved through the application of distributed model predictive control (DMPC) to better anticipate the effects of disturbances and real-time pricing together with following the modular structure of the system under control. To this end, the load side and heating system are decoupled through a three-element mixing valve, which enforces a fixed water flow rate in the building pipelines. Hence, the building temperature control is executed by a linear model predictive control, which in turn is able to exchange the building information with the heating system controller. On the contrary, there is a variable action of the mixing valve, which enforces a variable circulated water flow rate within the tank. In this case, the optimization problem is more complex than in literature due to the variable circulation water flow rate within the tank layers, which gives rise to a nonlinear model. Therefore, an adaptive linear model predictive control is designed for the heating system to deal with the system nonlinearity trough a successive linearization method around the current operating point. A battery is also installed as a further storage, in addition to the thermal energy storage, in order to have the option between the charging and discharging of both storages based on the electricity price tariff and the building and thermal energy storage inertia. A qualitative comparative analysis has been also carried out with a rule-based heuristic logic and a centralized model predictive control (CMPC) algorithm. Finally, the proposed control algorithm has been experimentally validated in a well-equipped smart grid research laboratory belonging to the ERIGrid Research Infrastructure, funded by European Union's Horizon 2020 Research and Innovation Programme

Experimental model validation and predictive control strategy for an industrial fire-tube boiler

This paper presents a flexible control structure for fire-tube boilers based on a suitable integration of the typical decentralized PI control structure and model predictive control technique. First, a dynamic nonlinear reference model of the fire-tube boiler is developed combining models available in the technical literature, based on first principle laws. The overall system model is considered as a gray-box model, and it has been validated with real data. Then, a suitable control-oriented model is derived out of the nonlinear reference model, in order to design a hybrid cascade MPC-PI control structure capable of guaranteeing stability, improving performances and enforcing real-time constraints. The flexibility of such a structure can be exploited to impose different types of functional behavior to the boiler itself, from the performance-related ones to the efficiency increase ones. While the reference non-linear model is large and detailed, the control-oriented one is simplified so that a few process parameters are identified to reduce dramatically the implementation in the MPC controller hardware and software framework. A sensitivity analysis with respect to these process parameters highlights the robustness and easy implementation of such a strategy. Two MPC configurations have been developed and tested in simulation over the validated boiler model. Then, a customized algorithm has been developed to understand a massive quantization phenomenon on the boiler pressure measurement. Finally, a test session conducted on a real fire-tube boiler quantifies the performance benefits of one configuration of the MPC-PI control structure with respect to the PI control

A Predictive Control Strategy for Energy Management in Buildings with Radiant Floors and Thermal Storage

Due to the growing energy demand in residential building, the need to reduce carbon footprint, and the smart grid paradigm, thermal energy control and overall power consumption reduction have become a hot research topic. The development of an energy management system able to modify consumer's energy consumption patterns while preserving comfort is a substantial solution. Hence, for load shaping in demand side management, particularly useful is the usage of a thermal energy storage (TES). It gives the possibility to shape the demand profile in an economic way based on dynamic electricity tariffs, by storing energy in thermal terms during off-peak hours. This paper focuses on the development of a novel control model for the integration of TES, HVAC system, building and local renewable energy sources to be used with optimization techniques. The presented control framework is based on Model Predictive Control (MPC) to better anticipate the effects of disturbances (e.g. weather conditions and user requirements on the load side, electricity price, etc.). A distributed structure has also been considered, to follow the modular structure of the system under control with the aim of optimizing the energy consumption costs and improving the indoor comfort level. Furthermore, the novel configuration of TES coupled with a heat pump and a radiant floor building giving rise to a more complex model with respect to the literature ones

Energy efficiency improvement for industrial boilers through a flue-gas condensing heat recovery system with nonlinear MPC approach

Recovering waste heat from boiler's exhaust flue gas has proven to be an effective way to improve energy efficiency in industrial boilers. Generally, a hydraulic design based on Condensing heat exchangers (CHXs) that boosts startup efficiency by increasing feed-water temperature and condensation rate in the CHX is suggested. In this paper, an innovative coupled CHX and heat accumulation system is presented. A detailed 1-D reference model of the system is formulated and validated by experimental data. Although the mentioned innovative heat recovery system alone is able to enhance the boiler's efficiency, the optimal performance extremely depends on the way it is operated. Furthermore, accurately estimating and predicting the condensation heat of the CHX is crucial for enhancing efficiency, yet the task is challenging due to the vapor content's phase change in the flue gas. To address these issues, a Nonlinear Model Predictive Controller (NMPC) is implemented, which utilizes a constrained optimal observer to handle the system's hybrid behavior. The performance of the proposed NMPC is evaluated against Linear MPC and conventional PID control. Results show that the proposed approach provides 48 [kg/h] saving in fuel consumption, which is about 6% more than the heuristic approach and 3.5% more than linear MPC technique

Sensitivity analysis of medical centers energy consumption with EnergyPlus

Sensitivity analysis plays a vital role in building energy analysis. It is done to clarify which are the crucial variables affecting building thermal performance and to evaluate quantitatively those effects. It can be conducted both through energy simulation models and real case observations. The present paper is devoted to the description of the sensitivity analysis techniques that are able to extract the most effective parameters on the energy consumption of a commercial building, particularly medical centers. Energy consumption in medical centers, generally, depends on several parameters ranging from technical and geometrical aspects to climatic conditions. This paper is focused on the application of sensitivity analysis in term of energy consumption in medical centers through a benchmark simulation model which is developed by National Renewable Energy Laboratory (NREL) in order to classify the most effective parameters on energy consumption of a large hospitals. © 2017 IEEE