Artificial Intelligence and Photovoltaics: how AI CARE optimises energy self-consumption

In recent years, the integration between photovoltaic systems and storage systems have transformed the way we produce and use energy. However, the real leap forward happens with the introduction of’Artificial intelligence applied to energy management.

In this context, it arises AI CARE, the software developed by HEIWIT to dynamically optimise the use of energy produced by photovoltaic systems with sodium-ion batteries.

What is self-consumption optimisation

For self-consumption This refers to the share of energy produced by a photovoltaic system that is used directly by the user, without being fed into the electricity grid.

In a traditional system:

• Energy is consumed when available (typically during the day)
• Excess energy is fed into the grid
• The energy needed when the sun is not producing is purchased from the grid

This model is inefficient because it does not take into account the optimal moments for use and valorisation of energy.

The role of artificial intelligence in photovoltaics

L’Artificial intelligence (AI) allows overcoming this limit by introducing predictive and adaptive energy management.

AI CARE uses advanced algorithms to analyse and correlate different factors:

• Projected photovoltaic production (based on weather data)
• User energy consumption Historical patterns
• Energy prices (e.g. hourly PUN trend)
• Battery status State of Charge

The result is a system capable of making autonomous decisions on:

• When Charge the battery
• When to download it
• When selling energy to the grid
• When minimise the purchase

Energy optimisation: from passive system to active system

Un impianto fotovoltaico tradizionale può essere definito un sistema passiveit produces energy but does not decide how to use it.

With AI CARE, the system becomes Active:

• It forecasts future production
• Anticipate consumption
• Optimise energy flows in real time

This approach is known as Intelligent energy management e represents one of the most significant evolutions in the renewable energy sector.

Key terminology (explained simply)

• SOC (State of Charge): Battery charge level expressed as a percentage
• National Single Price: price of electricity on the wholesale market, variable every hour
• Self-consumption energy produced and used directly
• Network entry Unused energy that is sold
• Peak shaving Reducing peak consumption through battery usage

The concrete advantages of AI applied to photovoltaics

The integration of AI CARE brings measurable benefits:

• Increased self-consumption
• Reduction in energy purchased from the grid
• Better valorisation of the energy produced
• Battery charge/discharge cycle optimisation

In particular, optimising the cycles also allows for preserve the useful life of the battery, a fundamental aspect in storage systems.

Perché le batterie agli ioni di sodio sono ideali per l’AI

HEIWIT batteries sodium ions present features that integrate perfectly with intelligent control systems:

• High thermal stability
• Absence of critical materials such as cobalt
• Good tolerance to frequent cycles
• Stable performance even at low temperatures

These properties allow the AI to operate with greater flexibility, without the typical limitations of some lithium technologies.

Conclusion

The adoption of the’Artificial intelligence in photovoltaics represents a key step towards more efficient, sustainable and autonomous energy systems.

AI CARE demonstrates how it's possible to transform a plant from a simple energy generator into Intelligent system capable of optimising every kWh produced.

In a context where energy costs and resource management are increasingly central, the evolution towards solutions of AI-based energy management it is no longer an option, but an inevitable direction.