• Energy Research

Abstract Model-based control schemes such as model predictive control (MPC) can assist smart-energy systems in achieving higher efficiency and utilization of renewable energy sources. A practical barrier for deploying such control schemes for space heating of residential buildings is the costs related to obtaining the weather data measurements needed for identifying a model that describes the dynamic behaviour of the building. Therefore, this paper reports on a simulation-based study investigating whether there is a significant impact on the performance of MPC schemes when substituting these weather measurements with data from meteorological weather services. Since access to weather forecasts is necessary during the operation of the MPC scheme, this implementation approach draws on data already available to remove the need for weather measurements. The results indicated that this approach only led to a minor performance impact in that heating savings were reduced by 4% while comfort violations increased by less than 0.1 Kh per day on average. The results thereby suggest that the use of data from meteorological forecast services for model identification may constitute a cost-efficient alternative to on-site or near-by weather measurements.

Abstract The application of building archetypes is a widespread approach used in urban building energy modeling. Working with archetypes has a range of benefits, but it is important that modelers avoid using oversimplified approaches when establishing the archetype as they lead to loss of uncertainty and, consequently, to models with inferior predictive capabilities. In this paper, we propose a multilevel take on the challenge of establishing archetypes. A simultaneous modeling and calibration framework is formulated using Bayesian inference techniques – a technique that allows for the propagation of uncertainty throughout the calibration process. By means of hierarchical modeling, information from training buildings is partially pooled together to form an optimal solution between separate building energy models and a completely pooled model. This enables the inference of uncertain archetype parameters that are less prone to building outliers than what is achieved using ordinary aggregation of individual building estimates. The proposed framework incorporates dynamic building energy modeling of arbitrary temporal resolution where uncertain parameters are fitted for individual building models and the archetype model simultaneously. The application of the framework is demonstrated using case-study data from the Danish residential building stock, containing 3-hourly measurements of energy use for 50 training buildings. The model is tested for the prediction of 100 out-of-sample test buildings’ aggregated energy use time series on a holdout validation period. With a prediction error of only NMBE = 2.9% and CVRMSE = 7.8%, the archetype framework promises well for urban modeling applications.

Abstract Existing simulation-based studies on applying model predictive control (MPC) schemes for space heating operation to enable demand response (DR) make use of linear models for the heating system, usually by assuming convective electrical baseboard heaters. However, buildings connected to district heating networks are typically equipped with hydronic heat emitters, such as radiators, that behave nonlinear. This paper therefore investigates the effect of including the nonlinear dynamics of a hydronic heat emitter on the DR potential of MPC for space heating. Furthermore, the performance of a practical two-level control approach suitable for real application, in which a heating setpoint was determined by a linear MPC and communicated to a conventional proportional integral controller, was investigated. The simulation framework for the investigation was based on the application of an experimentally obtained hydronic radiator model applied in different co-simulation setups, featuring a model of a poorly and a highly insulated apartment, respectively. The results indicated that inclusion of the nonlinear thermal effects of hydronic radiators did not significantly affect the DR performance when compared to the results of an MPC scheme controlling convective electrical baseboard heaters. In general, both setup achieved operational cost savings of approx. 5% and 18% in an existing and retrofitted building, respectively, while restricting the amount of thermal comfort violations to a limited extent. This suggests that results obtained in previous studies featuring electrical baseboard heaters also apply to buildings equipped with hydronic heating systems, and that future simulation-based studies and practical implementation of MPC for space heating can continue to rely on the use of far less computationally demanding linear control-models. Furthermore, the results suggest that the two-level control scheme seems like an appropriate control setup suitable for real applications.