Artificial Intelligence (AI): Friend of the environment?

Artificial Intelligence (AI), with its reputation for number crunching and thirst for processing power, is often considered a major threat to the environment. It is viewed as a behemoth of industrial processes that has a thirst for power that far exceeds its benefit to humanity. However, it is enlightening to realize that instead of bringing about the apocalyptic demise of the environment, artificial intelligence is, in reality, being used to optimize our use of renewable energy.

Solar energy has become one of the most important types of renewable energy technologies in the world today. As a result, significant investments have been made to improve its yield and make it competitive. Although costs have decreased, the instability of energy output due to photovoltaic panels’ dependence on solar radiation, weather conditions, temperature, and geographical location has presented significant challenges for energy market operators.

AI forecasting to stabilize energy production

To solve this problem, machine learning algorithms have been used to provide accurate forecasting of energy production. In addition to forecasting, AI-based technology has been implemented to improve power grids and storage units, using sensors to provide data that helps network operators improve control and flexibility. Drone technology is also being used to inspect solar panels, and market expansion is being explored.

AI-based forecasting models are one of the most common ways machine learning has been implemented in the field of solar energy. Machine learning algorithms are designed to recognize patterns in data sets, build mathematical models, and make decisions or predictions without being explicitly programmed. Support Vector Machines (SVM), gradient boosting decision trees, and hybrid models that combine machine learning methods and traditional statistical methods have been used to improve forecasting activities and stabilize solar energy production. Although no perfect model for forecasting exists, hybrid models are more accurate than pure machine learning models. The accuracy of each model varies based on climatic conditions, and a model trained with data collected from a specific site during the time of year for which predictions need to be made will work better than a generic model trained with a representative dataset of many weather regimes.

Storage

The use of AI extends beyond forecasting to power grids and storage units, which can be equipped with sensors to provide valuable data to network operators for greater control and flexibility. For example, combining energy flow control systems with large-scale energy storage units can provide network operators with the ability to stabilize the grid by storing energy during times of low demand and releasing it during periods of high demand.

AI & solar energy: A bright partnership

In terms of market expansion, AI is expected to play an increasingly important role in the development of solar energy markets. AI can be used to analyze and predict trends in energy prices, supply, and demand, and it can also be used to optimize the use of renewable energy sources, reducing the costs of energy production.

AI-based technology has emerged as a vital tool in the development and growth of solar energy. With the ability to provide accurate forecasting, improve power grids and storage units, inspect solar panels, and optimize the use of renewable energy sources, AI-based technology has made significant contributions to the industry. The role of AI in solar energy is expected to continue to grow, as advances in machine learning, deep learning, and other AI technologies continue to improve the industry’s efficiency and profitability.

With AI’s help, the renewable energy industry will continue to expand, providing a sustainable and environmentally friendly source of energy for generations to come.