• Energy Research

Abstract 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 °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.

Abstract Occupancy controlled heating, ventilation and air condition in offices are a mean to avoid energy waste due to over-conditioning of rooms. Occupancy detection methods based on sensor data that is already available in a building for other reasons than occupancy detection are therefore of great interest. However, such methods need to be validated according to a ground truth. This paper presents an image-based method for automatic establishment of a reliable and anonymous ground truth at entrance areas where the room height only allow the camera to be installed at 2.3–3.0 m above the floor. The method deploys various image processing techniques to infrared depthframe images recorded with a ceiling-mounted Kinect camera to anonymously detect and track persons entering or leaving a room and, consequently, counting the number of occupants in the room. The method demonstrated an accuracy of more than 98% in a stress test, 100% in a range of relevant function test and 99% in a three week long room occupancy test.

Abstract 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.

Abstract 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.

Abstract This paper reports on a simulation-based study that investigated the demand response potential of a model predictive controller (MPC) for space heating defined to minimize a weighted sum of electricity costs and CO2 emissions. The performance of the MPC was compared to a traditional controller and the results showed that an MPC with no weight on CO2 emissions reduced the total electricity costs, shifted consumption from high to low load periods and reduced consumption in the hour with the yearly maximum grid load; but it could also cause an increase in CO2 emissions. Contrary, the MPC with no weight on electricity costs reduced CO2 emissions; but it only reduced total costs marginally, it could cause a shift of consumption from low to high load periods and it increased consumption in the hour with the yearly maximum grid load. Finally, if the MPC used a weighted sum of electricity costs and CO2 emissions a range of intermediate results were obtained. The weighting factor can thus be used either to balance the performance of the MPC with respect to all performance indicators or to maximise it with respect to one indicator of particular interest.

Abstract 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, e max , 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’.