For an efficient indoor air purification, it is important to know the detailed airflow distribution in the room. A series of numerical simulations are carried out for five heating regimes using an air–air heat pump, capillary mat on the ceiling, capillary mat on the walls, heated floor, and radiator. The most homogeneous temperature field is obtained for the case with the heated floor. The highest velocity is obtained for the air–air heat pump, while the lowest is obtained for the capillary mat on the ceiling. A portable air cleaner based on the prototype device is introduced into the model and its influence on the velocity and temperature distributions is calculated. Our simulations additionally consider is the transport of an infectious aerosol and its purification inside the air cleaner. The time dependency of the concentration is exponential, and the purification rate depends on the air cleaner’s orientation and heating regime. The efficiency is higher for a purifier with flow in the upwards direction compared to in a horizontal one. In the experimental part, an NaCl solution is dispersed into the air, and the efficiency of purification in the case of the air–air heat pump is evaluated by measuring the time-dependent particle concentrations. These experimental results corroborate the numerical model.

With solar panels increasingly used in nearly zero energy building solutions it is important to clarify if the panels can achieve the efficiency indicators specified by their producers in real operation conditions. To determine the efficiency of poly- and monocrystalline panels depending on their spatial orientation and the seasons a set of test panels was installed in 2018 in Riga, Latvia for long-term monitoring of the amount of generated power and produced energy. Here we summarize the results of the first year of monitoring. Data indicate that orientation has high significance, very small amounts of energy are generated in autumn/winter and that there is a good correspondence of the actual efficiency to the standardized estimated values. The energy production is significantly reduced by the cloudiness that is characteristic for Latvia’s climate from October till January. To make a generalization of the results possible it is planned to continue the monitoring for several years.

The operational and indoor environmental quality of buildings has a significant impact on global energy consumption and human quality of life. One of the key directions for improving building performance is the optimization of building control systems. In modern buildings, the presence of numerous actuators and monitoring points makes manually designed control algorithms potentially suboptimal due to the complexity and human factors. To address this challenge, model predictive control based on artificial neural networks can be employed. The advantage of this approach lies in the model’s ability to learn and understand the dynamic behavior of the building from monitoring datasets. It should be noted that the effectiveness of such control models is directly dependent on the forecasting accuracy of the neural networks. In this study, we adapt neural network architectures such as GRU and TCN for use in the context of building model predictive control. Furthermore, we propose a novel hybrid architecture that combines the strengths of recurrent and convolutional neural networks. These architectures were compared using real monitoring data collected with a custom-developed device introduced in this work. The results indicate that, under the given experimental conditions, the proposed hybrid architecture outperforms both GRU and TCN models, particularly when processing large sequential input vectors.

In the present day, monitoring and automated control stand as pivotal factors for the energy-efficient and comfortable operation of buildings. As the demand for indoor climate control grows, building management systems have become more intricate, making their control challenging due to the increasing number of controllable elements. Replacing manual human analysis of complex systems can be achieved through the utilization of algorithms like model-based control. It is important to note that performance of this method usually relies on the accuracy of neural network-based building state forecasts. Studying the internal dynamics of climate as influenced by temperature changes necessitates a brief record of measurements, whereas evaluating structural modifications through moisture transfer demands data covering a more extended period. Neural networks such as Long Short-Term Memory have the potential to lose information within lengthy time-series data, and the intricate nature of moisture transfer further adds complexity to the task of approximating functions, ultimately leading to a reduction in energy efficiency. In order to improve the precision of indoor climate predictions, our suggestion involves not only assessing changes in temperature but also considering alterations in U-values triggered by temperature variations and moisture transfer. Our preliminary assessment of the influence of U-value, conducted through numerical simulations using WUFI6, exposes variations of up to 10 % of U-value in certain scenarios. Dealing with these computations in real time using physical models proves to be demanding due to computational requirements and limited data availability. To tackle this issue, we present an innovative preprocessing approach for on-the-fly evaluation of U-values. Empirical trials involving three years of monitoring data indicate that the suggested technique led to an approximate 8 % reduction in the average mean squared error of climate predictions based on neural network models, in specific instances.

This manuscript describes the development of a wireless sensor system for long term monitoring of temperature, humidity and heat flux reading within building structural elements, including places that are hard to reach using wired sensors. The system was tested in cold Latvian climate in 3 different buildings. The main objectives during the development phase were the maximization of network operational lifetime, ensurance of work stability and maintenance cost reduction to make the system feasible for wide use in practical applications. An optimal radio module and microcontroller combination yielded sufficient signal range and data transfer stability, as well as efficient control of energy consumption.

Buildings consume about 40 % of all energy. Ventilation plays a significant role in both the energy consumption of buildings and the comfort of occupants. To achieve energy efficiency and comfort, smarter ventilation control algorithms can be employed, such as those with feedback based on CO₂ levels. Furthermore, by knowing the current number of people in a space, ventilation can theoretically be adjusted to maintain a constant CO₂ level without wasting energy when people are not present. An additional benefit of such control could arise due to occupants’ habits. For example, if a person senses elevated CO₂ levels, even if the ventilation system has started operating more intense, they might choose to open a window, potentially compromising energy efficiency. Therefore, if the control algorithm were to maintain a constant CO₂ level, occupants may be less likely to open windows. In our work, we explore a model in combination with a custom monitoring system based on computer vision to implement such control. The monitoring system combines outside and inside CO₂ sensors with precise people counting based on computer vision to provide data to the model. The model relies on the mass balance equation for CO₂ and considers the historical data of the number of occupants and their activities to estimate the overall CO₂ generation in indoor spaces. The results suggest that the model can effectively forecast CO₂ dynamics with an absolute deviation of 40 ppm. However, it was observed that the analysis of the actual air exchange level could be compromised by several factors.