In recent years, the global energy market has undergone a tremendous transformation. Considering the pressing challenges such as climate change, the energy companies and government are realizing the need for a more sustainable solution and bringing major disruptions in the sector. The International Energy Agency (IEA) forecasts India is going to overtake the European Union by becoming the world's third-largest energy consumer by 2030. Further, the exploding population, economy, urbanization and industrialization will result in India's energy needs growing at three times the global average. This makes difficult for companies to generate energy only by using traditional resources and brings the need to adopt renewable energy resource to bridge the gap between high demand and supply gap.
As the world moves towards the solar energy revolution, technologies like Artificial Intelligence have become significant solutions to cater to the growing energy needs. Like every other industry, AI's potential will be able to observe trends by analysing the vast quantities of data and optimize present and future challenges. As a consequence, there will be increased efficiency in the conversion and distribution of energy. Furthermore, the accurate forecasting and analyzing data, AI will allow mitigation of risks and drive operational excellence in crucial areas.
AI has the potential to unlock the hidden possibilities of solar energy that can create low carbon economy at a fast pace. Its accuracy in forecasting helps in maintaining system reliability and performing complex tasks at a speed.
Installation of AI integrated smart meters
To create an efficient solar power system, accuracy in demand forecasting is necessary. Adoption of AI in solar power systems plays a role in the demand side management by optimizing economic load dispatch. Increased installation of AI integrated smart meters synthesizes an enormous amount of data to the providers to make timely decisions on the allocation of energy resources. For instance, AI uses deep learning algorithms to analyse energy consumption patterns from historical data and predict future demand that helps the energy provider make right decision about demand response.
Real-time updates and maintenance
Renewable energy resources are always fluctuating as they largely depend on weather conditions. AI being a predictive technology has the potential to forecast weather conditions and equipment failure. Further, AI drives the optimization of solar power systems through maximized efficiency and real-time monitoring. This plays a crucial role in the proactive maintenance of infrastructure. Combined with Machine Learning technology, infrastructure maintenance works through anomaly detection and automated monitoring. These real-time updates and data offer insights into the working of the grid through predictive intelligence. These algorithms further improve the safety and reliability of the installed solar power systems.
Improvement in design and manufacturing of solar panels
The design and manufacturing of solar power systems depend on a variety of factors such as weather conditions, geographical placement and consumption patterns. Advanced technologies such as AI and ML understand the amalgamation of these factors in a way that helps in the productive installation of solar panels. Additionally, the amount of data gathered and analysed by these technologies also help in designing of the infrastructure of solar power system. Such futuristic technologies successfully analyse the infrastructure design that help in right decision making by identifying optimal layout of solar panels. As a result, providers can save optimize cost in the initial stage itself by determining the number, size and design of the panels that need to be installed for a well-behave system.
Optimization of transmission and distribution networks
The maintenance of transmission and distribution networks is one of the most pressing challenges in the energy sector. Generating and distributing energy with renewable resources become more complex with the fluctuating in solar energy. Another layer of complexity that comes into the picture is the integration of solar energy into traditional grids. AI combined with ML algorithms works on anomaly detection that indicate technical glitches in the grid, theft or change in consumption patterns for a healthy distribution network. Moreover, it helps energy providers to maximize the usage of renewable energy resources and eliminate the wastage beforehand.
Bottomline
The vast potential of AI and other predictive
technologies can drive the sector towards innovation. From predicting the
future demand to auto maintenance, these technologies help in advancing the
sector and balancing demand and supply in the competitive market. Additionally,
the reliability in delivery models meet the consumer demand better than
traditional methods while keeping the costs under control.
