A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> Abstract Conventional ventilation control schemes for VAV systems cannot achieve acceptable indoor air quality (IAQ). Ventilation control at the zone level is one of key factors affecting IAQ and also impacts thermal control. This paper proposes a multi-input–multi-output (MIMO) controller to control temperature and ventilation of multiple zones in a building with a model predictive control (MPC) strategy. The controller follows the ventilation rate procedure of ASHRAE Standard 62.1 and meets its ventilation requirements. Simulation-based experiments under four types of typical weather conditions are conducted to evaluate the controller's performance. The experimental results demonstrate that the controller is capable of maintaining ventilation air requirements and temperature of multiple zones. <s> BIB001 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> This paper describes the development and evaluation of a model-based approach for minimizing peak cooling demand using energy storage inherent in building structures. On any day where the strategy is invoked, the building is precooled with zone temperature setpoints at the low end of comfort prior to a demand-limiting (DL) period. The zone temperatures are then adjusted upwards during the demand-limiting period following a trajectory that keeps the peak cooling load below a specified target. The cooling demand target and setpoint trajectory are determined using a building model that is trained using field data. The overall approach was demonstrated for a building representative of a small commercial facility. The first step involved training the inverse model using a few weeks of hourly data. The model was then used to study the potential for peak load reduction and to determine setpoint trajectories that were implemented at the site. The demand-limiting strategy resulted in approximately 30% reductions in peak cooling loads compared to night setup control for a 5-h on-peak period of 1 PM to 6 PM. <s> BIB002 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> 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. <s> BIB003 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> This paper describes an optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices. The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy-consumption level, maximum and minimum hourly load levels, and ramping limits on such load levels. Price uncertainty is modeled through robust optimization techniques. The model materializes into a simple linear programming algorithm that can be easily integrated in the Energy Management System of a household or a small business. A simple bidirectional communication device between the power supplier and the consumer enables the implementation of the proposed model. Numerical simulations illustrating the interest of the proposed model are provided. <s> BIB004 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> Abstract A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC). The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations. <s> BIB005 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> A model based predictive control method is applied in order to determine the optimal supply fluid temperature in the case of concrete embedded water-based floor heating in low energy residential buildings The aim of the control is to keep the indoor temperature within a defined comfort interval The forthcoming supply fluid temperature is obtained through a numerical optimisation based on a prediction of the upcoming heat demand The elementary response function which is the basis for the method is obtained from a numerical control volume model and as an alternative from a simplified 2-node lumped model The accuracy of the results obtained from the simplified model is surprisingly good in comparison to the detailed numerical model The control method is applied for a single room for which a perfect prognosis of the heat demand exists The results show a fairly steady optimised supply fluid temperature. <s> BIB006 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated. <s> BIB007 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> The problem of modeling and stochastic optimization for home energy management is considered. Several different types of load classes are discussed, including heating, ventilation, and air conditioning unit, plug-in hybrid electric vehicle, and deferrable loads such as washer and dryer. A first-order thermal dynamic model is extracted and validated using real measurements collected over an eight months time span. A mixed integer multi-time scale stochastic optimization is formulated for the scheduling of loads of different characteristics. A model predictive control based heuristic is proposed. Numerical simulations coupled with real data measurements are used for performance evaluation and comparison studies. <s> BIB008 </s> A Survey on Home Energy Management <s> 3) MODEL PREDICTIVE CONTROL <s> Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively. <s> BIB009
HEMSs scheduling can be regarded as a receding-horizon optimal control problem, such as MPC. At current discrete time k, a horizon-dependent optimal control sequence is computed based on current states sampled from the system, being only the first control action implemented on the system. At next time step k + 1 this procedure is once again repeated. MPC supports dynamic modelling and disturbance prediction BIB007 , BIB008 , BIB009 , BIB004 , useful to address problem uncertainties. One of the drawbacks of MPC-based techniques is related to the stability of the underlying closed loop system and the sub-optimality of the corresponding control system. Nevertheless, MPC has been reported to achieve a satisfactory performance regarding the energy management of buildings, outperforming other control schemes, such as in the case of heating BIB006 , BIB005 , cooling BIB002 , BIB003 and ventilation BIB001 . However, MPC-based methodologies involve very often significant modelling costs, as they require a detailed plant model, rely on data acquisition from the plant, along with the implementation of required observers, expert monitoring and deployment. This has been pointed out as a limitation regarding the application of MPC-based technologies in medium to large buildings BIB009 .