• Energy Research

The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention.

A daylighting control system that uses the closed loop proportional algorithm needs to be calibrated both during nighttime and daytime. The selection of the daytime calibration time can affect the behavior of the system and is usually performed when the ratio of the illuminance of the ceiling sensor (SD,tc) to the illuminance at a point on the working plane (ED,tc) is relatively large without sunlight patches in the ceiling photo-sensor’s field of view (FOV). However, this requirement is not associated with a specific value and can be achieved under a wide range of conditions related to the sky luminance distribution. In the present work, four ceiling sensors with different field of views (FOVs) were examined in a typical north-facing office space. The effect of daytime calibration on the system’s performance was estimated through the calculation of lighting energy savings and the overdimming percentage. The results show that the effect of both the FOV of the ceiling sensor and the daytime calibration period is small except for the case of the sensor without cover, especially when it is close to the opening. In an attempt to quantify the SDtc/IDtc ratio, a new magnitude (RR) is proposed by dividing the illuminance ratios of the ceiling photo-sensor by that on the working surface during daytime and nighttime calibration. Thus, the daily calibration of the sensors with cover can be performed when RR > 1.

Abstract Thermostats control the heating, ventilation and air-conditioning (HVAC) system of a building based on the temperature they measure. Integration with communication network technologies allows wireless sensors to be used as the temperature sensing component of an HVAC system, increasing the flexibility in the selection and positioning of sensors. This study compared the temperature measuring performance of nine wireless and two conventional wired temperature sensors against reference air and globe temperature sensors in a climate chamber with a two-person office setup. The influence of sensor position, room cooling system (all-air or radiant with ventilation) and cooling load (33, 61 W/m2) was studied. Sensors placed at the same position had a measurement difference of up to 1.8 K, and assumptions about the type of temperature a sensor measures (air or globe) had the largest impact on the deviation from the reference temperatures. As opposed to common assumptions, conventional wired temperature sensors measured closer to globe temperature sensors and could be a possible indicator for the operative temperature. When the load settings were high, measurements in radiant system cases had smaller deviations from the reference sensors compared with all-air systems, due to the chilled surface compensating for the radiation from the loads.

This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases. The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump, the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%. Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation.