In the heart of Poland, researchers are pioneering a method that could revolutionize how buildings manage their energy needs, particularly when it comes to harnessing the power of the sun. Arkadiusz Małek, from the Department of Transportation and Informatics at WSEI University in Lublin, has developed an innovative approach to acquiring and processing data from photovoltaic (PV) systems, potentially transforming the way we think about energy management in institutional buildings.
Małek’s research, published in the journal *Applied Sciences* (translated from Polish as *Applied Sciences*), focuses on a 50 kWp photovoltaic system installed at his university’s administrative building. The system not only generates energy but also collects vast amounts of data, which Małek argues is key to optimizing energy use. “The challenge is not just in generating energy but in effectively processing the data to gain insights that can help manage energy more efficiently,” he explains.
The crux of Małek’s method lies in unsupervised clustering, a form of artificial intelligence that groups data points based on their similarities without prior training. This allows for the creation of ‘signatures’ for both the energy generated by the PV system and the energy consumed by the building. By analyzing these signatures in a three-state space—essentially a visual representation of the data—Małek can quickly determine the power generated and the power needed to run the building.
This approach has significant implications for the energy sector. For instance, it could enable buildings to optimize their energy use in real-time, reducing waste and lowering costs. “The applied approach can have a wide practical application, both in energy management in institutional buildings,” Małek notes. Moreover, the method could be used to train AI algorithms to categorize operating states in a Smart Grid, paving the way for more intelligent energy management systems.
The potential commercial impacts are substantial. As buildings increasingly adopt renewable energy sources, the ability to manage these systems efficiently will become a key competitive advantage. Małek’s research could help energy companies and building managers make data-driven decisions, ultimately leading to more sustainable and cost-effective energy use.
Beyond the immediate applications, Małek’s work also opens up new avenues for research. For instance, the use of AI-assisted algorithms to optimize energy processes in real-time could lead to advancements in Advanced Process Control, a field focused on improving industrial processes. “Such expert-validated knowledge is highly desirable in Advanced Process Control,” Małek says, hinting at the broader implications of his research.
In the rapidly evolving energy sector, Małek’s work stands out as a beacon of innovation. As buildings strive to become more sustainable and energy-efficient, his method could play a pivotal role in shaping the future of energy management. And with the growing interest in renewable energy sources, the timing couldn’t be better. As Małek puts it, “The traditional and AI-assisted algorithms used by the authors are used to obtain practical information about the operation of Smart Grid.” This research is not just about managing energy; it’s about reimagining the way we interact with it.