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Future of Energy Landscape at a Glance
Energy landscape has evolved fairly with time. From thermal to nuclear power sources, it has traveled a long journey; still, it has a long path to go. Decarbonization of energy space has been strived for long. Although nuclear power plants have found grounds, still there is much left to do to achieve the same. Apart from nuclear technology domination, there are several other transformations like digitalization that will reshape the energy landscape in the proximate future.
Decentralization of Energy
Smart grids, solar plants, and various other technological advancements have led the power industry to decentralization. Various minds comprehend it variously. For some it is the behind-the-meter investment, some others believe, embedding of power generation and storage in the distribution network. Renewable energy sources have also added to it leading to decentralization trend to become ubiquitous in future. Earlier batteries fulfilled instantaneous response required but now its saturation allowing batteries and smart grids to alleviate traditional grid constraints. All of these factors together are veering the power industry towards energy decentralization and further leading to energy trading, similar to financial trading.
Age of Digitalization
The dawn of digital age has already taken place and has spread among all industrial landscapes, redefining the operational activities through automation. Similar effects are being observed in the energy landscape where bill payments, recharges, and various other process have gone digital. Artificial intelligence (AI) is taking the automation a step further by adding the feature of prediction. In the proximate future, AI is evident to digitalization to new heights in the power industry.
Prediction and Maintenance: Fault prediction and machinery maintenance are two obvious implementations of AI in power landscape. AI combined with smart sensors can effectively predict the faults prior to occurring allowing predictive maintenance and scheduled downtimes. In-home device faults can also be predicted through the same to avoid major appliance loss.
Optimizing Cost Investment: Energy organizations invest heftily in quest of new power sources, still there is low assurance of success. AI can analyze various seismic images and geological data for the same and provide results defining the chances of success. Investments can be made accordingly or shifted to other sources rather than on one with low probability.
Energy Efficiency: Automation has a big role to play for efficient energy usage. Gadgets automated through AI can comprehend the need of energy for a particular job or even could turn themselves off when not in use, saving a vast amount of power.