The pursuit of climate neutrality requires global systemic actions involving the use of solutions aimed at reducing emissions. Changes must be introduced in all sectors affecting climate change, namely power engineering and district heating, construction, transport, and industry, as well as agriculture and forestry. Analyzing the structure of final energy consumption in the EU by sector, it can be stated that households account for 27% of the total energy consumption. Comprehensive actions are needed to increase the energy efficiency of buildings. The aim of this paper was to indicate aspects of improving energy efficiency in buildings and their equipment, taking into account the striving for climate neutrality. Analyzed possibilities and conditions of using various solutions of energy-efficient systems aimed at increasing energy resilience and security and preventing environmental degradation. Particular attention was paid to construction and material solutions, as well as installation solutions, which increased the accumulation and energy efficiency of the building. These activities are closely related to the conditions and dynamics of the heat exchange process in the applied solutions and are also related to the factors influencing thermal comfort and energy consumption in buildings. Due to the growing popularity of modern information technologies and artificial intelligence in energy management in recent years, this article reviews the latest research in this area. One of the directions of future research indicated by scientists is autonomous building control in real time, adapting to the momentary needs of users. The analysis of the possibilities of using modern energy efficiency solutions in buildings conducted in this work may be useful for optimizing heat and energy management models and models of society’s consumption as an element of energy transformation towards climate neutrality and counteracting the deepening of energy poverty.

The accurate prediction of heat demand in retrofitted residential buildings is crucial for optimizing energy consumption, minimizing unnecessary losses, and ensuring the efficient operation of heating systems, thereby contributing to significant energy savings and sustainability. Within the framework of this article, the dependence of the energy consumption of a thermo-modernized building on a chosen set of climatic factors has been meticulously analyzed. Polynomial fitting functions were derived to describe these dependencies. Subsequent analyses focused on predicting heating demand using artificial neural networks (ANN) were adopted by incorporating a comprehensive set of climatic data such as outdoor temperature; humidity and enthalpy of outdoor air; wind speed, gusts, and direction; direct, diffuse, and total radiation; the amount of precipitation, the height of the boundary layer, and weather forecasts up to 6 h ahead. Two types of networks were analyzed: with and without temperature forecast. The study highlights the strong influence of outdoor air temperature and enthalpy on heating energy demand, effectively modeled by third-degree polynomial functions with R² values of 0.7443 and 0.6711. Insolation (0–800 W/m²) and wind speeds (0–40 km/h) significantly impact energy demand, while wind direction is statistically insignificant. ANN demonstrates high accuracy in predicting heat demand for retrofitted buildings, with R² values of 0.8967 (without temperature forecasts) and 0.8968 (with forecasts), indicating minimal performance gain from the forecasted data. Sensitivity analysis reveals outdoor temperature, solar radiation, and enthalpy of outdoor air as critical inputs.