Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Better or even optimal building energy control and operation strategies provide great opportunities to reduce building energy consumption. Moreover, it is estimated by the National Energy Technology Laboratory that more than one-fourth of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. Energy forecasting models for building energy systems are essential to building energy control and operation. Three general categories of building energy forecasting models have been reported in the literature which include white-box (physics-based), black-box (data-driven), and gray-box (combination of physics based and data-driven) modeling approaches. This paper summarizes the existing efforts in this area as well as other critical areas related to building energy modeling, such as short-term weather forecasting. An up-to-date overview of research on application of building energy modeling methods in optimal control for single building and multiple buildings is also summarized in this paper. Different model-based and model-free optimization methods for building energy system operation are reviewed and compared in this paper. Agent based modeling, as a new modeling strategy, has made a remarkable progress in distributed energy systems control and optimization in the past years. The research literature on application of agent based model in building energy system control and operation is also identified and discussed in this paper.

Review of building energy modeling for control and operation

https://depositonce.tu-berlin.de/bitstream/11303/11904/2/Bloess_etal_2018.pdf

Power-to-heat for renewable energy integration: A review of technologies, modeling approaches, and flexibility potentials

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.

/pdf/model-predictive-control-mpc-for-enhancing-building-and-hvac-3bxzsgstnj.pdf

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

This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literatu ...

https://zenodo.org/record/895978/files/article.pdf

On heat pumps in smart grids: A review

Building modeling as a crucial part for building predictive control

Study of the optimal control problem formulation for modulating air-to-water heat pumps connected to a residential floor heating system

Model predictive control (MPC) is a good candidate to exploit the energy cost savings potential of concrete core activation (CCA), while guaranteeing thermal comfort. A bottleneck for practical implementation is the selection and identification of the building control model. Using grey box models, this article studies the impact of model structure and identification data set on the MPC control performance for an office building with CCA. Results for a one-year simulation show: (1) a second-order model can achieve equal control performance as a fourth-order one, (2) inclusion of solar or internal gains in the identification data set improves the model accuracy in general, especially for the fourth-order models and (3) MPC with a second-order model reduces electricity consumption by 15% compared to a reference controller, hereby deploying information about past operative temperature prediction errors and this without the need for solar or internal gains predictions.

Building models for Model Predictive Control of office buildings with Concrete Core Activation

The current study investigates the optimal operation of an air-to-water heat pump system. To this end, the control problem is formulated as a classic optimal control or dynamic optimization problem. As conflicting objectives arise, namely, minimizing energy cost while maximizing thermal comfort, the optimization problem is tackled from a multi-objective optimization perspective. The adopted system model incorporates the building dynamics and the heat pump characteristics. Because of the state-dependency of the coefficient of performance (COP), the optimal control problem (OCP) is nonlinear. If the COP is approximated by a constant value, the OCP becomes convex, which is easier to solve. The current study investigates how this approximation affects the control performance. The optimal control problems are solved using the freely available Automatic Control And Dynamic Optimization toolkit ACADO. It is found that the lower the weighting factor for thermal discomfort is, the higher the discrepancy is between the nonlinear and convex OCP formulations. For a weighting factor resulting in a quadratic mean difference of 0.5°C between the zone temperature and its reference temperature, the difference in electricity cost amounts to 4% for a first scenario with fixed electricity price, and up to 6% for a second scenario with a day and night variation in electricity price.

/pdf/multi-objective-optimal-control-of-an-air-to-water-heat-pump-4g3az4typ9.pdf

Multi-objective optimal control of an air-to-water heat pump for residential heating

Model Predictive Control of Ground Coupled Heat Pump Systems in Office Buildings (Modelgebaseerde regeling van grondgekoppelde warmtepompsystemen in kantoorgebouwen)

This paper proposes to apply the celebrated Proper Orthogonal Decomposition (POD) approach in order to obtain a short-term control model for the temperature dynamics of the borehole system. The main idea behind POD-based modeling is the extraction of a set of optimal basis functions containing the main characteristics of the system from snapshots of the system dynamics at various time instants. This way, POD allows an accurate description of the heat diffusion process where the POD modes can be used as states of the control model. The obtained control model is used for control of heat extraction and injection.

Clara Verhelst

Papers

Study of the optimal control problem formulation for modulating air-to-water heat pumps connected to a residential floor heating system

Building models for Model Predictive Control of office buildings with Concrete Core Activation

Multi-objective optimal control of an air-to-water heat pump for residential heating

Model Predictive Control of Ground Coupled Heat Pump Systems in Office Buildings (Modelgebaseerde regeling van grondgekoppelde warmtepompsystemen in kantoorgebouwen)

Development of a control-oriented model for borehole dynamics for buildings equipped with ground coupled heat pumps