Reducing the energy consumption of a data centre while maintaining the requirements of the compute resources is a challenging problem that requires intelligent system design. It even becomes more challenging when dealing with an operating data centre. To achieve that goal without compromising the working conditions of the compute resources, a temperature model is needed that estimates the temperature within the hot corridor of the cooling system based on the properties of the external weather conditions and internal conditions such as server energy consumption, and cooling system state. In this paper, we discuss the dataset creation process as well as the process of evaluating a model for forecasting the temperature in the warm corridor of the data centre. The proposed solution compares two new neural network architectures, namely Time-Series Dense Encoder (TiDE) and Time-Series Mixer (TSMixer) with classical methods such as Random Forest and XGBoost and AutoARIMA. The obtained results indicate that the lowest prediction error was achieved by the TiDE model allowing to achieve 0.1270 of N-RMSE followed by the XGBoost model with 0.1275 of N-RMSE. The additional analysis indicates a limitation of the use of the XGBoost model which tends to underestimate temperature as it approaches higher values, which is particularly important in avoiding safety conditions violations of the compute units.

There are many papers concerning the consumption of energy in different buildings. Most describe residential buildings, with only a few about office- or public service buildings. Few articles showcase the use of energy consumption in specific rooms of a building, directed in different geographical directions. On the other hand, many publications present methods, such as machine learning or AI, for building energy management and prediction of its consumption. These methods have limitations and represent a certain level of uncertainty. In order to compare energy consumption of different rooms, the measurements of particular building-room parameters were collected and analyzed. The obtained results showcase the effect of room location, regarding geographical directions, for the consumption of energy for heating. For south-exposed rooms, due to sun radiation, it is possible to switch heating off completely, and even overheating of 3 °C above the 22 °C temperature set point occurs. The impact of the sun radiation for rooms with a window directed east or west reached about 1 °C and lasts for a few hours before noon for the east, and until late afternoon for the west.

The inspection of PAPI (precision approach path indicator), ALS (approach light system), and runway lights is the basis of maintenance actions that safeguard the safety of airport operations. These actions consist of measurements requiring the accurate positioning of reference viewing points. Commonly, GNSS-based methods are employed and augmentation procedures are applied to improve the positioning precision. Augmentation involves the use of additional sources of information such as reference stations and complicates the process of position calculations. An image processing-based method is proposed for triangulating the positions of reference viewing points during inspections of airfield lights using a UAV. The Structure-from-Motion (SfM) photogrammetric range imaging technique and airport geodesic data are the basis of the method. The measurement of PAPI transition angles is presented as an application example. The results are compared to GNSS-based measurements and prove the viability of the method.