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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.

The present study aims to satisfy the energy demands of a set of Norwegian residential structures with the least carbon dioxide and most renewable energy. Real-time data on building domestic hot water (DHW), heating, and electricity usage is used to plan and expand the renewable energy supply side. PVT panels provide DHW and electricity in the hybrid solar and biomass energy system. Heat is produced by the digester and heat pump. Also used is a twofold effect absorption refrigeration system for cooling. Rule-based regulation manages heat streams and redirects flows on the supply side. We provide the plant's size and execute the dynamic energy simulation. Electricity and biomass expenses determine building heating. The system is then tuned for operational conditions and compared to the design point. PVT may generate over 80% of annual DHW. Summertime radiation is more intense and can be turned into cooling energy, therefore 64.8% of cooling output comes from it. Digester/CC heats 66.55% of the structure, suggesting designers use biomass in winter due to increased energy costs. A parametric analysis shows that increasing PVT duration and tank size affects efficiency and emissions differently. Cost, efficiency, and emission index at TOPSIS are 9.73 $/hr, 36.8%, and 7.75 kg/MWh, according to optimization findings.

Maintaining acceptable indoor air quality within operating rooms (ORs) in healthcare facilities is vital to prevent surgical site infections (SSIs). Two prevalent ventilation systems, laminar airflow (LAF) and mixing ventilation (MV), are widely used in ORs for various surgical operations. However, several studies have stated that these systems have slightly different SSI rate controls. Earlier studies discovered the airflow velocity above the surgical incision in LAF-equipped ORs to be twice as high as in MV-equipped ones. In addition, the placement of surgical lamps and different airflow patterns add complexity to the study of the surgical microenvironment. The objective of this study is to characterize the airflow pattern in the surgical microenvironment by computational fluid dynamics (CFD) simulations and experimental measurements in the newly built operating room lab at NTNU. The findings may provide new guidelines for standards development ensuring safer surgeries in healthcare facilities. The research project is called: Reduction of postoperative surgical site infection (POSI) through the development of XR tool. The project is supported by the Norwegian Research Council and Norconsult.

Accurate long-term forecasts of aggregate energy load profiles are crucial for effective energy system planning at regional and national scales. This study aims to validate PROFet, a flexible load profile modeling tool. PROFet forecasts weather-dependent heating and electrical load profiles at an hourly resolution for both residential and non-residential buildings connected to district heating systems. Given that the tool’s accuracy for residential buildings has been demonstrated in previous studies, further validation is needed for non-residential buildings. To achieve this, our study evaluates PROFet’s extrapolation performance using an out-of-sample test dataset from Trondheim municipality. The results show that PROFet accurately forecasts the hourly heating load during the space-heating season but is less accurate during periods when domestic hot water needs dominate. The tool exhibits nearly identical performance across the three cases, except for efficient kindergartens, where the estimation accuracy is lower.

Model predictive control (MPC) is an advanced optimal control technique to minimize a control objective while satisfying a set of constraints and is well suited to activate the building energy flexibility. The MPC controller performance depends on the accuracy of the model prediction. Inaccurate predictions can directly lead to low control performance. Linear time-invariant (LTI) models are often used in MPC in buildings. However, LTI models do not adapt to the weather conditions varying throughout the whole space-heating season, which makes the MPC based on LTI models not perform well over a long period of time. Therefore, this study introduces an adaptive MPC where the parameters of a linear grey-box model are continuously updated in real-time. Two alternative versions of this adaptive control are investigated. The first one, called partially adaptive MPC, only updates the effective window area of the grey-box model, while the second one, called fully adaptive MPC, updates all the parameters of the grey-box model. Results show that the partially adaptive MPC is not able to deliver satisfactory prediction performance. The fully adaptive MPC shows better performance compared to the other models when implemented in a MPC, especially in avoiding thermal comfort violation.

The evaporation rate from the swimming pool is a main parameter influencing the energy use in swimming facilities. Quantifying this phenomenon is crucial when modelling the facility in Building Performance Simulation. This study investigates the accuracy of ASHRAE equation using field measurements. This equation is widely used and implemented in BPS tools, such as the pool model in IDA ICE. The investigated dataset was based on two different indoor swimming pool facilities in Norway. It includes in total 75 swimming lessons (i.e., occupied pool) and 477 hours of unoccupied operation. While ASHRAE recommend 1.0/0.5 for occupied and unoccupied pools, respectively, the average activity factor was found to be 0.7 and 0.8 for the investigated occupied pools, with a maximum at 1.1. Moreover, the activity factor was between 0.50 and 0.57 for unoccupied pools.

To evaluate the performance of RANS turbulence models, this study compares four different cavity flow benchmarks using the prevailing two-equation turbulence models for indoor airflows, namely the standard and RNG k-ε and the standard and SST k-ω models. A cavity flow consists of one air inlet and one outlet slot. The inlet slot is positioned on the upper left corner of the cavity, whereas the outlet slot is located in the lower right. This cavity flow is representative of mixing ventilation. These four cavity benchmarks differ by their geometry (i.e., the aspect ratio of the room), flow regime and whether the flow is isothermal or not. Measurements of the air velocity and temperature in these benchmarks are used to evaluate the accuracy of the RANS turbulence models. Many existing studies have investigated the airflow and heat transfer over these benchmarks. However, the numerical methods and other relevant CFD parameters are not always described in detail, reducing the transparency and reproducibility of these works. To compare the influence of the RANS turbulence model on the four cavity flows, a same CFD setup is adopted here for all benchmarks. This setup is based on the best practice in RANS, namely a steady second-order spatial discretization on a wall-resolved structured mesh and with a grid convergence analysis. The results show that k-ε models, particularly the standard k-ε model, are best suited in a fully turbulent flow regime without strong pressure gradients. On the opposite, the SST k-ω model performs best in the transitional regime while the k-ε models only give moderate to poor results.