Cost-efficient measurement of room-level heat output from hydronic radiators is a major barrier to large-scale implementation of Economic Model Predictive Control (EMPC) in residential space heating for demand-side management. This paper therefore presents a novel EMPC strategy for hydronic radiators that relies on measurements of radiator pipe temperatures as a proxy for the radiator heat output, thus eliminating the need for costly flow-based meters at each radiator in a building. Simulation-based experiments indicate that the proposed proxy-based EMPC matches the performance of its heat-based counterpart. The proxy-based EMPC achieved a 16.6% cost reduction compared to the heat-based EMPC's 16.8%, with no comfort violations in both cases. Furthermore, the strategy shows resilience towards uncertainties in the user-estimated radiator exponent and maximum heating capacity. The proposed EMPC scheme also allows system operators to fine-tune the balance between cost savings and return temperatures using the proxy's upper limit. The findings presented in this paper suggest that the proposed proxy-based EMPC scheme provides a practical pathway for broader applications of EMPC in hydronic-based space heating with the prospect of unlocking significant load shifting potential, cost savings for end-users, and enhanced efficiency in individual and collective energy systems.

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

Recent research has indicated a significant potential for providing demand response using Economic Model Predictive Control (E-MPC) of radiators in residential space heating systems. Previous studies indicate the need for distinct temperatures in different rooms, which would require an E-MPC with different temperature constraints across the dwelling. Studies on E-MPC concepts for this purpose often assume that it is possible to measure the heating power delivered to each room separately. However, this assumption is not valid in typical existing residential buildings heated by hydronic radiators; they only have one heat meter measuring the heating use of the entire housing unit. This study presents a simulation-based study of a novel dual-zone E-MPC scheme that relies on indoor air temperature measurements in individual rooms of the housing unit, and only one heat meter measuring the total heating use for space heating and domestic hot water. The results indicate that the proposed scheme has the same load shifting potential as an E-MPC using room-level heat metering. The proposed scheme thus reduces the additional hardware and data infrastructure needed for the practical realization of E-MPC of residential space heating systems for demand response purposes.

Recent simulation-based studies have indicated that load shifting using Economic Model Predictive Control (E-MPC) of radiators for residential space heating can be utilized for demand response purposes in energy systems. However, there is a lack of studies on whether this load shift potential can be realized in real, inhabited buildings. This paper reports on a field experiment aiming at realizing load shifting of space heating in a single-family house with a hydronic radiator system connected to the local district heating. Radiators in a limited number of 'active' rooms were controlled using set-point schedules mim-icking the typical behavior of E-MPC. The results indicate that it was possible to load shift heating con-sumption in the 'active' rooms but that the load shift for the building as a whole was limited due to the hydronics of the radiator system. The paper also reports on the several practical issues encountered dur-ing the experiments - issues that in different ways are barriers to the practical realization of demand response from E-MPC of hydronic space heating systems.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

A grey-box model is a combination of data-driven and physics-based approaches to modeling. For applications in buildings, grey-box models can be used as the control model in model predictive control (MPC) or to characterize the thermal properties of buildings. In a previous study using data generated from virtual experiments, the influence of data pre-treatment on the performance of grey-box models has been demonstrated. However, field measurement differs from data generated using building performance simulation (BPS). This is because the precision and accuracy, the location, and the dynamics of the sensors could be different. Consequently, this paper extends previous results and conclusions using a real test case of a highly-insulated residential building. The results confirm that data pre-processing has a minimal influence on the identified results (parameter values and simulation performance) for deterministic models. On the contrary, data pre-treatment influences the performance of stochastic models as follows. Firstly, large sampling time (Ts) can cause the parameters to become non-physical and can sometimes reduce the one-day ahead prediction performance. With large Ts, the anti-causal shift (ACS) proves to be beneficial to keep the parameters physically plausible while low-pass filtering can also contribute but to a lesser extent. With large Ts, ACS does not guarantee a higher one-day ahead prediction performance for stochastic models, whereas pre-filtering generally has a positive impact. Secondly, for the stochastic model, the sensor dynamics should be modeled if the sensor has a noticeable time constant to guarantee the physical plausibility of the parameters. Thirdly, the dynamics of the hydronic radiator do not need to be modeled if the time constant in the temperature sensors is larger than the radiator. These findings provide practical guidelines for grey-box modeling of buildings with field measurement data.

Several studies have indicated a significant potential in using Model Predictive Control (MPC) of space heating for demand response purposes. The performance of the MPC depends on the predictive performance of the embedded control model. The studies often employ black- or grey-box control models; however, no previous studies consider whether a black- or grey-box model is more robust against weather changes. To assess this, the simulation-based study reported in this paper analysed how the predictive performance of black- and grey-box models trained with different input–output datasets from a certain period of a year is affected when subject to weather conditions in other periods of the year. The predictive performance of the grey-box models was slightly better compared to the black-box model. Furthermore, the grey-box models were slightly more robust to changes in weather data. Future studies should investigate whether the differences have practical significance in relation to MPC.

Previous studies have identified significant demand response (DR) potentials in using economic model predictive control (E-MPC) of space heating to exploit the inherent thermal mass in residential buildings for short-term energy storage. However, the economically viable realisation of E-MPC in residential buildings requires an effort to minimise the need for additional equipment and labour-intensive modelling processes. This paper reports on an experiment where a novel E-MPC setup was used for thermostatically control of a hydronic radiator in a highly-insulated residential building located on the NTNU Campus in Trondheim, Norway. The E-MPC utilized data from a heating meter, two temperature sensors and an existing weather forecast web service to train a linear black-box model. The results showed that the precision of model trained on excitation data that was generated using setpoints of either 21 or 24 degrees C was sufficient to obtain good control of the indoor air temperature while shifting consumption from high to low price periods. The findings of the experiment indicate that a minimal E-MPC setup is able to realize the significant DR potential that lies in utilizing the inherent thermal mass in residential buildings.

Previous studies have identified a significant potential in using economic model predictive control for space heating. This type of control requires a thermodynamic model of the controlled building that maps certain controllable inputs (heat power) and measured disturbances (ambient temperature and solar irradiation) to the controlled output variable (room temperature). Occupancy related disturbances, such as people heat gains and venting through windows, are often completely ignored or assumed to be fully known (measured) in these studies. However, this assumption is usually not fulfilled in practice and the current simulation study investigated the consequences thereof. The results indicate that the predictive performance (root mean square errors) of a black-box state-space model is not significantly affected by ignoring people heat gains. On the other hand, the predictive performance was significantly improved by including window opening status as a model input. The performance of black-box models for MPC of space heating could therefore benefit from having inputs from sensors that tracks window opening.

Model Predictive Control (MPC) is a key technology to activate the building energy flexibility. A reliable control-based model should be developed for each specific building. The structure of grey-box models is usually based on the physical knowledge of the building. Firstly, it is not certain that this information will be available for all buildings, especially for small residential buildings. Secondly, developing a specific model structure for each building is most probably not affordable. Therefore, the paper investigates the dependency on the model structure to create reliable control-oriented model for the thermal mass of residential buildings. Using a test case, the performance of grey-box models based on the physical knowledge of the building are compared to grey-box models based on a generic structure taken from building standards (EN 13790 and VDI 6007) as well as black-box models where no knowledge of the building is required.

Dynamic solar shading devices are an effective mean to avoid overheating in buildings due to excessive solar heat gains. They can also be used to affect the heat balance of buildings; they can be on e.g. during nighttime to reduce space heating or retracted to increase night cooling. The shading control system can utilize this feature to obtain energy cost savings and reduce peak consumption. However, it is difficult to define optimal rule-based control strategies that minimize overheating and energy costs simultaneously. In this paper, we therefore propose an economic model predictive control (E-MPC) scheme for space heating where solar shading is included as an additional control variable. The proposed scheme employs black-box models, as they are 'cheap' to create from data; this is important for a widespread deployment of E-MPC in practice. The study was based on co-simulation experiments where EnergyPlus models represented the actual building and it was hereby demonstrated that the proposed E-MPC outperformed two rule-based controllers with respect to reducing overheating, energy costs and peak consumption. Shading also turned out to be valuable for north facing rooms where it reduced costs and peak consumptions compared to an MPC without shading. The energy-related benefits of the proposed E-MPC scheme can be regarded as 'an added value' that can be used for economic justification of an investment in dynamic solar shading devices. (C) 2019 Elsevier B.V. All rights reserved.

Several studies have indicated that Model Predictive Control (MPC) of space heating systems can utilize the thermal mass of residential buildings as short-term thermal storage for various demand response purposes. Realization of this potential relies heavily on the accuracy of the model used to represent the thermodynamics of the building. Such models, whether they are grey box or black box, are calibrated using relevant data obtained from initial measurements, and the performance of the calibrated model is validated using data from a subsequent period. However, many studies use validation periods with weather conditions similar to those of the calibration period. Only a few studies investigate whether the calibrated model performs satisfactory when subjected to significantly different conditions. This paper presents data from a simulation-based study on the effect of seasonal weather changes on the performance of a black-box model. The study was conducted using 11 years of Danish weather data (2008-2018). The results indicate that the performance of the black-box model deteriorate as the weather data conditions become increasingly different from those used in the initial model calibration. Further, the results show that calibration in heating season leads to satisfactory model performance through the heating season, but lower performance in transitional seasons (especially spring). Results also show that calibration in February led to highest model performance through heating season, while calibration in March led to satisfactory model performance in the whole heating and fall season.

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. (C) 2018 Elsevier B.V. All rights reserved.

In Denmark, the first district heating (DH) plant was built in 1903 and today about 64% of all residential buildings are connected to DH. The demand for DH is steadily increasing in and around major cities as more buildings are built for the increasing population. As a result, larger peak demands occur that might exceed the current DH production and/ or distribution capacities. To avoid or minimise this, existing thermal capacities in the city could be utilized to shift loads from peak to nonpeak periods and hence reduce or avoid the rather large public infrastructure investments needed to expand the existing DH capacities. Such existing capacities could be for example design containers, swimming pools, underground aquifers, soils, and lakes.This study investigates the potential of applying a public indoor swimming pool for load shifting. Simulations of alternative control scenarios were performed in EnergyPlus which effectively reduced the peak demand of a district heating area.

Sensitivity analysis (SA) can be applied to building energy models (BEM) to identify which input parameters that drive the majority of the model output variation. The screening-based Morris method is often applied for this purpose; however, consideration regarding the effect of the user-defined number of levels (p) and trajectories (r) on the obtained results are rare. This paper investigates how the choice of p and r affects the outcome of a SA using the Morris method on a high fidelity BEM. The results indicates that the Morris method was not able to replicate the ranking from the variance-based Sobol' method no matter the choice of r and p. It was, however, able to identify groups of input parameters (parameter clusters) most sensitive to the model output variability, but it required significantly more r than usually applied in studies featuring the Morris method. The reason is that marginal differences in absolute values of elementary effects (the sensitivity indices of the Morris method) for some input parameters may lead to a change in ranking position several times as the number of r increases. Users of the Morris method must therefore not be predetermined on the size of the parameter cluster; instead, they must make a visual assessment of the convergence of the parameter ranking to qualitatively determine the appropriate size of parameter cluster. The final recommendation for future studies deploying the Morris method for SA applied to a high fidelity BEM is to choose p >= 4 as it seems to lead the analysis towards a more truthful ranking, and then run simulations in steps of r = 100 when making the visual assessment to determine convergence and the size of parameter cluster. The identified need for more r questions the general notion that the Morris method is a computationally efficient screening method in terms of absolute time use. However, the Morris method is still much more computational efficient than a Sobol'-based analysis if the purpose of the SA is to identify a cluster of input parameters most sensitive to the model output variability. (C) 2018 Elsevier B.V. All rights reserved.

Addressing thermal comfort is an important aspect of applying economic model predictive control (E-MPC) schemes with the objective to perform demand response (DR), e.g. minimize operational cost. This paper compares the performance of four E-MPC schemes using both single-objective and multi-objective formulations to address thermal comfort. It is difficult to proclaim the superior formulation as the notion of thermal comfort is a subjective matter. However, the single-objective problem formulation proposed in this paper contains a parameter, εmax, which describes the maximum acceptable deviations from the preferred indoor air temperature. This parameter can be regarded as a user-defined indicator of the acceptable deviations from the preferred temperature or, in other words, their 'DR willingness'.

Several studies have indicated that work performance can be used as an indicator that articulates the relation between humans and indoor climate in office buildings. But does this knowledge affect the optimal office building design? This paper presents a method for simulation-based investigations on the extent to which optimisation of the relation between indoor climate (whole-body thermal comfort and perceived air quality) and productivity, instead of – or in combination with – comfort based acceptance criteria, affect the cost-optimal design of office buildings. For this purpose, a single-objective optimisation problem was formulated and a calculation procedure was proposed. The results of a retrofit case study indicate that energy use and productivity loss can be reduced if building designers optimise with respect to productivity instead of comfort based constraints. Optimising productivity while respecting comfort based constraints led to a less but still profitable solution. The composition of an economic optimal retrofit solution thereby strongly depends on whether the building owner is willing to put an economic value on the effect of the retrofit solution on comfort and/or the relation between indoor climate and productivity.

In ultra-low temperature district heating the supply temperature is less than required to heat the domestic hot water and a heat pump is therefore often proposed to raise the temperature. This paper investigates how this heat pump can be utilized for price based demand response to induce peak reductions and energy cost savings. A model predictive control strategy is proposed and evaluated through co-simulations where a model predictive controller is formulated in MATLAB and connected to an EnergyPlus hot water storage tank. It is demonstrated that the system is capable of reducing the district heating morning peak and the electric grid evening peak as well as providing energy cost savings for the end-user without compromising hygiene and comfort. (C) 2017 Elsevier B.V. All rights reserved.

Model predictive control is a promising control scheme to utilize space heating in buildings for price-based demand response. It is, however, crucial to have an adequate thermal model of the building in order to make this work. It is often very time-consuming to construct these models and common system identification approaches suggest experimental input signals that are difficult to obtain in practice, which often leads to thermal discomfort. This paper proposes an alternative approach where an initial model is identified from historical data and then later re-identified based on control input generated from a model predictive control using the initial model.

Heat transfer between apartments can challenge the positive effects of applying model predictive control (MPC) in multi-apartment buildings. This paper reports on an investigation of how the performance of two different MPC approaches – centralized and decentralized – may be affected by non-insulated and insulated partition walls between apartments. The results suggest that ignoring inter-zonal thermal effects using the less complicated decentralized approach leads to insignificant performance reductions compared to the more complicated centralized approach – especially if partition walls are insulated.