Optimizing the Future of Energy With AI
Energy-related carbon dioxide emissions hit a record high in 2018. Emissions rose “1.7% to reach a historic 33.1 gigatons of carbon dioxide” according to a report by the International Energy Agency (IEA). As global energy demands increase at exponential rates, many scientists have started turning to renewable energy sources as a feasible and reliable alternative to fossil fuels and other conventional sources that continue to release greenhouse gases into the atmosphere. Although over the years renewable energy has made several advancements in technology, there are still several prevalent roadblocks that continue to prohibit further progress. But with the help of machine learning and AI, renewable energy will hopefully be able to surmount these obstacles, providing an era in which this technology can interpret the past, optimize the present, and anticipate the future.
While there are several challenges that surround the issue of renewable energy, perhaps the most significant challenge is the unpredictable nature of the weather. The two major sources of renewable energy, solar and wind power largely depend on the weather for maximum effectiveness and energy storage. Even with the precise and accurate weather forecasting we have today, there is always the possibility of extreme and unexpected changes in the atmosphere which can create a detrimental influence on the flow of energy. With such vulnerabilities, it is essential that renewable energy companies begin to support AI research in an effort to advance both the efficiency and productivity of such sources.
The electric grid is one of the most complex systems on the planet, and is quickly advancing with the expansion of variable sustainable power sources. However, the present electric grid faces numerous obstacles in accommodating renewable energy. It is almost non negotiable that the industry must start utilizing “smart systems” to improve the incorporation of renewable sources into current electric grids, in order to make sustainable energy an equivalent player in the global energy supply. With the integration of AI, smart systems would be able to help with the amalgamation of microgrids and effectively manage the distribution of energy. Usually, when community level renewable energy generation units are added to the primary grid, it can become difficult to stabilize the energy flow of the electric grid. AI-powered control systems can thus play a significant part in deciphering quality and congestion issues from within.
What can be decided as the biggest achievement of AI in the realm of sustainability, however, is its ability to oversee the intermittency and unpredictability that comes with renewable energy sources, offering both improved safety and reliability. With AI technology, researchers will be able to better comprehend energy consumption patterns, identify the energy leakage and health of the devices in a much more efficient manner. For example, the AI-powered predictive analysis can collect the data from wind turbine sensors to monitor daily wear and tear. The system would be able to monitor the overall health of the equipment and alert the operator when maintenance is needed.
As the effects of climate change become more severe, we must be able to sustain ourselves through clean energy sources. Although renewable energy does have a few drawbacks, advancements in technology with the use of artificial intelligence can help to improve its efficiency and productivity, ultimately allowing it to gradually become a more practical solution to an enormous problem.