How does artificial intelligence support photovoltaics?

Artificial intelligence is assuming an ever more critical role in driving the advancements of the fourth industrial revolution. The energy sector, in particular, is increasingly embracing the possibilities AI offers. This ongoing transformation goes beyond the gradual integration of renewable energy sources (RES) into the energy mix. It encompasses comprehensive process enhancements, automation, data acquisition, and predictive endeavors. In these realms, AI is tackling challenges that were once considered beyond our reach.

AI-driven technologies substantiate their effectiveness and adaptability. This is notably evident in photovoltaics, where they lend support to virtually every facet of the domain? spanning from component manufacturing to the operation of fully functional systems. The energy sector’s active adoption of AI reflects the profound impact it has in optimizing operations and driving innovation within the industry.

AI in the production of PV modules

Perovskites and artificial intelligence – this combination embodies the potential for a transformative revolution in the field of photovoltaics. Pioneering work is being conducted by researchers at MIT and Stanford University, unveiling a groundbreaking solution.

The creation of solar cells based on perovskite demands the optimization of numerous variables simultaneously. Current laboratory processes rely on techniques like spin coating, but these are inefficient when scaled for industrial production. The ongoing research is set to alter this landscape.

The envisioned manufacturing method involves mobile surfaces, akin to sheets, onto which perovskite compounds are mechanically sprayed. This material remains in motion throughout, progressing through successive production stages. The analogy used is that of continuous spray painting.

So, what role does artificial intelligence play in this innovation? It becomes the orchestrator, dynamically adapting the process in response to ever-changing parameters. These parameters encompass aspects such as the composition of raw materials, temperature, humidity, processing speed, nozzle distance, and curing techniques.

Furthermore, the research team devised a way to infuse external insights, like empirical experiences of researchers, into machine learning. This was facilitated through the application of probability factors, specifically Bayesian statistics.

The synergy between spin coating technology and artificial intelligence yields the potential to vastly expedite the production of perovskite modules. This acceleration promises a favorable impact on the commercial integration of perovskite technology, setting the stage for a transformative shift in the photovoltaics sector.

Designing and selling even easier

Artificial intelligence is also making significant strides in the design and sale of photovoltaic installations, as exemplified by the Polish startup EasySolar.

EasySolar’s platform empowers users to craft compelling designs (visualizations) of solar panels on rooftops and land, perform economic analyses, and generate final offers for clients. The platform includes a dedicated CRM system tailored to the photovoltaic industry. Presently, AI is integrated into the CRM tool to analyze sales processes. It empowers users to identify the optimal sales trajectory within their company and gauge how individual factors impact sales conversion. However, EasySolar’s ambitions extend beyond this, aiming to harness AI for a broader spectrum of functionalities. According to Marcin Dolata, CEO of EasySolar, the goal is to evolve from a platform that facilitates photovoltaic system design and calculations into one that actively aids users throughout the process.

EasySolar streamlines the entire process, from design to sale, facilitating efficient navigation of the market and alignment with customer needs.

Designed for companies engaged in selling photovoltaic installations, EasySolar enables the swift preparation of installation visualizations and enticing customer offers in a matter of minutes. This translates to saving several hours in the sales process, with attractive offers enhancing sales prospects. Additionally, the application enhances sales process planning, consequently elevating sales conversion rates within companies. In sum, EasySolar optimizes time usage and augments the sales of photovoltaic installations.

Further AI implementations are on the horizon for EasySolar, with plans for enhancing AI support in PV system design expected to roll out in the third quarter of this year. This progression continues to underscore the innovative and transformative potential of AI in the renewable energy sector.

How to maximize energy yields thanks to AI?

In the realm of photovoltaic farms, maximizing energy yields stands as a paramount objective. Artificial intelligence emerges as a potent ally in achieving this goal. Notably, a noteworthy initiative backed by the German Ministry of Economy and Climate Protection exemplifies this potential.

The Solar Farm 2.0 project is poised to explore the realms of performance management at the level of strings and even individual photovoltaic modules. Collaborating on this endeavor are key players: the University of Karlsruhe, BRC-Solar, and PREMA.

For the experimental phase, two 30-kilowatt PV installations will be employed. One of these will integrate the innovative HiLEM (High Efficiency Low Effort MPP Tracker) system, while the second will operate as a standard connection, serving as a reference.

By leveraging the data amassed during this field trial, the project aims to develop artificial intelligence that can generate performance projections for photovoltaic systems. This initiative underscores the potential for AI to enhance the efficiency and output of photovoltaic farms, contributing to the evolution of renewable energy solutions.

Diagnostics and troubleshooting

Artificial intelligence and drones make for an impeccable partnership, particularly when it comes to diagnosing issues and malfunctions within photovoltaic systems.

In the realm of photovoltaic farms, there exist market-ready solutions that automate the inspection process. Drone-enabled thermal inspections amass a reservoir of data, which is subsequently processed by artificial intelligence systems. This involves the application of neural networks and deep learning.

These AI systems meticulously analyze thermal images captured by drones in near real-time. They promptly alert operators to any anomalies within the functioning of a given installation. This dynamic process significantly reduces response times and service execution periods. Furthermore, it extends into predictive diagnostics, enabling the identification and rectification of issues before they escalate into major problems.

The impact of this combination is considerable, especially during peak electricity production periods. Any downtime experienced by a photovoltaic farm equates to tangible losses for the investor. By curbing such interruptions, AI-driven drone inspections generate substantial savings and bolster operational efficiency across the renewable energy landscape.

Safe and efficient system

The leading cause of fires in photovoltaic installations arises from electric arcs, characterized by ionized air glowing due to voltage flashes between closely positioned electrical conductors or between a conductor and ground.

To mitigate the risk of electric arcs, monitoring methods involve measuring current or voltage frequency. These analyses often encompass parameters like frequency point, energy, and variance. Yet, there are instances where these methods falter, such as in parallel or ground arc detection scenarios.

Here, artificial intelligence emerges as a savior, increasingly integrated into photovoltaic inverters. This equips inverters with the ability to learn and recognize new arc patterns within their database. Consequently, the device becomes adept at distinguishing genuine electric arcs from mere noise, thereby nearly eliminating false alarms.

Leveraging AI, the efficacy of such technologies skyrockets, potentially reaching a remarkable 100% success rate. This paradigm showcases AI’s transformative impact in enhancing safety and reliability within the photovoltaic sector.

An inseparable element of RES development

Undoubtedly, artificial intelligence is set to assume a progressively pivotal role in advancing photovoltaics and other renewable energy domains. Its influence is already pervasive across all facets of investment implementation.

Furthermore, AI holds potential as a catalyst in enhancing grid flexibility within nations characterized by advanced renewable energy and electromobility landscapes. This entails the imperative of automated and streamlined energy management, a necessity in these contexts.

The integration of AI signifies not only a transformative trajectory for the renewable energy sector but also the emergence of sophisticated solutions that address the dynamic challenges of renewable energy integration and management. As the synergy between AI and renewable energy deepens, the future holds promises of more sustainable and efficient energy systems.