The study addresses the problem of short-term forecasting of ice temperature in engineering systems with high sensitivity to thermal loads. A transformer-based architecture is proposed, enhanced with a physics-informed loss function derived from the heat balance equation. This approach accounts for the inertial properties of the system and aligns the predicted temperature dynamics with the supplied power and external conditions. The model is tested on data from an ice rink, sampled at one-minute intervals. A comparative analysis is conducted against baseline architectures including LSTM, GRU, and Transformer using MSE, MAE, and MAPE metrics. The results demonstrate a significant improvement in accuracy during transitional regimes, as well as robustness to sharp temperature fluctuations—particularly following ice resurfacing. The proposed method can be integrated into intelligent control loops for engineering systems, providing not only high predictive accuracy but also physical interpretability. The study confirms the effectiveness of incorporating physical knowledge into neural forecasting models.

Keywords: short-term forecasting, time series analysis, transformer architecture, machine learning, physics-informed modeling, predictive control

Amid the climate crisis and rising energy costs, the need for improving energy efficiency is becoming increasingly critical. Governments aim to reduce carbon dioxide emissions, while businesses seek to optimize energy expenses. The digitalization of the energy sector and the adoption of Internet of Things (IoT) technologies create favorable conditions for the application of artificial intelligence (AI) in energy consumption management. This article provides an overview of AI technologies and their application in energy consumption management, using an ice rink as a case study. The energy consumption data collected from a real-world facility is analyzed, and methods of neural network modeling of time series for forecasting and optimizing management are examined. The results of the modeling are presented, demonstrating the potential of predictive algorithms in reducing energy costs and improving the operational efficiency of ice rinks.

Keywords: global warming, energy consumption, energy efficiency, digitalization, Internet of Things, artificial intelligence, energy management, machine learning, deep learning, time series, predictive algorithms