This article is a short and concise introduction to how Artificial Intelligence (AI) can be used effectively in the solar energy industry, speeding up many processes and improving accuracy. Some crucial applications of AI as well as more effective solar installations are mentioned below.
Artificial intelligence (AI) refers to a wide range of software systems and applications that can acquire new skills and develop their inner workings depending on previous experiences. It should come as no surprise that AI has also entered the energy industry, where it performs intelligent tasks to improve the safety, efficiency and potential of energy transmission, distribution and consumption systems.
According to the International Energy Agency (IEA), solar energy is expected to be responsible for 60% of the increase in global renewable capacity by 2022. To further maximize the effectiveness and reliability of renewable energy, many companies are utilize the benefits of AI technologies. Since AI can significantly contribute to the modeling, analysis and prediction of renewable energy weather and performance, AI is best suited to solve problems posed by renewable energy variability.
Solar energy systems can be improved with AI algorithms
AI is very useful in industries with significant amounts of data, and with energy being one of them, by utilizing training data, AI can spring into action and produce discoveries that can substantially help energy storage systems. Some examples of AI applications related to demand and generation estimates, solar panel inspection, wind and solar forecasting, electrical grids and storage units are shown below.
Wind and solar forecasts
AI can increase the accuracy of solar forecasting while reducing operating costs of power generation, starting and stopping expenses of traditional generators, and controlling solar energy curtailment.
AI also uses a combination of meteorological and satellite data, numerical weather forecasting models, and statistical analyzes to estimate wind and solar energy projections. Many energy storage options are used to compensate for the unpredictability of wind and solar energy production, which is undoubtedly a crucial aspect.
Before the development of AI, most forecasting methods relied on specialized weather models that provided a limited perspective on the factors influencing accessibility to renewable energy. Now, self-learning weather models, collections of historical weather data, real-time measurements from nearby weather stations, sensor networks, and cloud information collected from satellite photographs and sky cameras have all been integrated into AI systems.
Demand and generation forecast
Accurate forecasts help the system operate safely and reliably, balancing energy supply and demand. They can also significantly increase the operational efficiency of energy storage devices. If an energy supplier knows how much energy will cost at each hour of the day, it can store it during the cheapest times and release it at peak times.
AI is very effective in anticipating energy production and consumption, hence the price at a given time. This is achieved by analyzing multiple data sources, including years of historical electrical data and weather data – especially hot or cold days require more air conditioning or heating, which drives electricity consumption.
Inspection of solar panels using drone technology
The use of robotic technology can assist with remote inspection and maintenance tasks. When autonomous drones continuously analyze solar panels, they can collect data at least 50 times faster than manual approaches, while increasing operational safety. Drones can detect manufacturing flaws, cracks and other issues by collecting data with dedicated thermal cameras, which can then be used to assess the severity of problems.
AI to improve power grids and storage units
AI can be used to perform super-detailed modeling of energy use in buildings, such as predicting passive solar capacity, wind speed, and building energy load. We can, for example, automatically turn off ovens when energy is low, integrating energy flow management systems.
AI is already transforming smart grids into fully intelligent grids, enabling them to meet future energy demands. Electrical networks can be equipped with a variety of sensors that capture a large amount of data, which, when evaluated by artificial intelligence, provides useful information to network administrators, allowing greater control and flexibility.
The integration of AI in identifying important areas for solar installations
The role of AI could also be crucial in determining areas for solar installations. Because solar panels are installed on rooftops, calculating the solar potential of each rooftop or inland body of water can be time-consuming and expensive. However, with the help of machine learning, both indoor and rooftop solar installations become easier and faster. How else can AI be useful in solar installations?
- Using annotation tools, we can access a labeled dataset of roofs and then determine their types and structures.
- Using image segmentation, AI can help analyze individual roofs and identify areas available for solar panels by subtracting areas with obstacles from the whole.
- It can determine the quality of roofing materials and give the probability of how the material will affect the installation process.
- The AI will also decide the best direction of the solar panel so that it can generate more energy by having many hours of exposure to light per day.
- Remote sensing techniques can be applied to satellite imagery to infer the depth of inland water bodies and then use the data for floating solar installations.
Key Takeaways
Artificial intelligence allows a machine, robot or device to imitate people's cognitive activity and is evolving rapidly across industries. The solar energy industry, which provides a reliable alternative source of energy, is no exception.
As the world strives to make our environment greener and more sustainable, the use of AI-based applications can ensure a safe and smooth energy transition, which will represent an investment in saving our planet. There is no denying that in addition to the benefits and advantages of solar energy, it also faces many challenges that slow down the process.
Artificial Intelligence (AI) and machine learning have become essential technological solutions in this industry, potentially solving most of today's challenges. Currently, studies have been involved in applying artificial intelligence methods to energy and renewable energy systems, and the results are not disappointing.
About the author-
Melanie Johnson-
AI and computer vision enthusiast with extensive experience in technical writing. Passionate about innovation and AI-based solutions. Loves sharing expert insights and educating individuals about technology.
