The sun’s total radiation alone exceeds the world population’s entire energy consumption by 7.500 times and ignites secondary renewable energy sources. The end energy consumption buildings use for heating amounts to 28% of Germany’s total energy consumption. With the ongoing trend of digitalization and the transition of the German energy supply away from fossil fuels and the consequent political dependency, electric heat pumps and photovoltaic (PV) systems have become increasingly important to the discussion. This has led to an increasing demand for smart control strategies, especially for inert systems such as thermally activated building systems (TABS). This paper presents and analyses a weather predictive control (WPC) strategy using a validated thermodynamic simulation model. The literature review of this paper outlines that the current common control strategies are data intense and complex in their implementation into the built environment. The simple approach of the WPC uses future ambient temperature and solar radiation to optimize the control of the heating, cooling, ventilation, and sun protection system. The thermal comfort and energy demand evaluate the concept. We show that with a WPC for TABS, thermal comfort can improve without increasing the energy demand for the office building in the moderate climate of Munich. Furthermore, this paper concludes that the WPC works more effectively with more thermal mass. This simplified building control strategy promotes the European roadmap goal of climate neutrality in 2050, as it bridges the phenomenon of the performance gap.

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Monitoring individual exposure to indoor air pollutants is crucial for human health and well-being. Due to the high spatiotemporal variations of indoor air pollutants, ubiquitous sensing is essential. However, the cost and maintenance associated with physical sensors make this currently infeasible. Consequently, this study investigates the feasibility of virtually sensing indoor air pollutants, such as particulate matter, volatile organic compounds (VOCs), and CO2, using a long short-term memory (LSTM) deep learning model. Several years of accumulated measurement data were employed to train the model, which predicts indoor air pollutant concentrations based on Building Management System (BMS) data (e.g., temperature, humidity, illumination, noise, motion, and window state) as well as meteorological and outdoor pollution data. A cross-validation scheme and hyperparameter optimization were utilized to determine the best model parameters and evaluate its performance using common evaluation metrics (R2, mean absolute error (MAE), root mean square error (RMSE)). The results demonstrate that the LSTM model can effectively replace physical indoor air pollutant sensors in the examined room, with evaluation metrics indicating a strong correlation in the testing set (MAE; CO2: 15.4 ppm, PM2.5: 0.3 μg/m3, VOC: 20.1 IAQI; R2; CO2: 0.47, PM2.5: 0.88, VOC:0.87). Additionally, the transferability of the model to other rooms was tested, with good results for CO2 and mixed results for VOC and particulate matter (MAE; CO2: 21.9 ppm, PM2.5: 0.3 μg/m3, VOC: 52.7 IAQI; R2; CO2: 0.45, PM2.5: 0.09, VOC:0.13). Despite these mixed results, they hint at the potential for a more broadly applicable approach to virtual sensing of indoor air pollutants, given the incorporation of more diverse datasets, thereby offering the potential for real-time occupant exposure monitoring and enhanced building operations.