The four directions of the energy transition
The transformation of the energy system is commonly described along four main lines:
- Decarbonisation: the progressive abandonment of fossil fuels in favor of renewables is a process now underway. Photovoltaic, wind and other clean sources are becoming predominant in new installations, with direct impacts on the design and management of buildings.
- Decentralization: energy production is moving from large centralized systems towards distributed systems: photovoltaic systems on roofs, cogeneration, local micro-grids. This approach reduces network losses and increases system resilience.
- Democratization: the citizen is no longer just a consumer, but also a producer of energy (“prosumer”). Energy communities, collective self-consumption and storage systems are changing the role of end users.
- Digitalisation: Sensors, smart meters and monitoring systems generate large amounts of data. Their correct processing allows us to understand how energy is actually used in buildings.
Where artificial intelligence makes the difference
Artificial intelligence finds concrete application in various areas of energy management. For example:
- Forecasting production from renewable sources: photovoltaic production is strongly influenced by weather conditions. AI models can integrate weather forecasts, historical data and seasonal parameters to estimate expected production with good accuracy, supporting load and self-consumption planning.
- Balancing production and consumption: in buildings equipped with distributed generation and storage systems, AI allows you to optimize the use of locally produced energy. The analysis of consumption profiles and usage habits allows you to decide when to accumulate energy, when to use it and when to withdraw it from the grid, reducing energy costs.
- Intelligent system management: an “intelligent” building does not simply react to a fixed set-point. AI-based systems learn occupancy profiles, take into account weather forecasts and the building’s thermal inertia, dynamically adjusting heating, cooling and lighting. The result is an improvement in comfort with lower consumption.
Services already available for the construction sector
Research and operational applications have led to the development of services that can already be used today:
- forecasting of energy consumption of buildings;
- evaluation of climate-normalized post-intervention savings;
- support for energy requalification decisions;
- optimization of micro-grids and storage systems;
- reduction of load peaks;
- dynamic management of thermo-hygrometric comfort.
These services are particularly useful for technicians and administrations who must make decisions based on objective and measurable data.
The crucial issue of algorithmic transparency
One of the main obstacles to the adoption of artificial intelligence is the so-called “black box”: models that produce results but do not explain how they got there. For those who work in public administration or technical design, this approach is problematic: a system that suggests an investment or estimates savings must be able to explain the factors considered and the relative weight of each.
In recent years, the paradigm of explainable artificial intelligence has been establishing itself, which allows forecasts to be associated with an understandable description of the determining variables. This aspect is fundamental for user trust and for the technical validation of choices.
Practical implications for professionals and PAs
For technicians in the sector, AI represents an advanced support tool for energy diagnoses, predictive maintenance and intervention planning. For public administrations, it allows them to reduce the energy costs of public buildings, improve investment planning and transparently document the results obtained in terms of savings and reduction of emissions. For occupants, the benefits translate into greater comfort and lower running costs.
Artificial Intelligence applied to the energy management of buildings is not a futuristic technology, but a tool that is already available and constantly evolving. Its value lies not in replacing technical skills, but in the ability to amplify them, providing more accurate analysis and decision support. The challenge is not technological, but cultural and organizational: data quality, interdisciplinary skills and model transparency are the key factors for an effective diffusion of these solutions in the construction sector.
