With climate change occurring at a severe pace, scientists and professionals have listed sectors and industries where AI and ML can be leveraged to solve many environmental issues like reduction in emissions, wastage of energy, and pollution.
FREMONT, CA: The top nine high leverage range of sectors where ML can be utilized to curb the mismanagement of energy and reduce wastage are:
1. Improving Predictions of Energy Necessary:
ML algorithms can be optimized by including specific factors and real-time functioning to make the predictions more precise. The forecast on energy demand can be used in the utility sector to initiate better management of renewable resources.
2. Discover New Materials:
With the utilization of ML, scientists can collect data faster to analyze, design, and evaluate chemical structures to create new materials which can store, harvest, and supply energy efficiently.
3. Optimize Shipping Routes:
ML can work out the most energy saving and efficient routes for shipping of goods worldwide. It can also combine shorter trips or decrease the number of trips to formulate a resilient system of transportation.
4. Encouraging Electric-Vehicles:
The battery energy management in electric vehicles can be optimized by ML to maximize the mileage per charge and predict the aggregate charging behavior to assist in better placements of grids to manage the load capacity flexibly.
5. Creating Smart Buildings:
A smart building with intelligent control systems can reduce the overall energy consumptions and also communicate with the grid to maintain the supply of low-carbon electricity supply.
6. Quality of Data For Predictions Increased:
For the effective mitigation of energy wastage, it is necessary to be aware of the quantity of energy wasted per unit. Computer vision technologies can identify the problems and report where energy savings can be increased by maximizing efficiency.
7. Optimize Supply Chains:
ML can optimize the food, fashion, and consumer goods supply chains by minimizing the inefficiencies and carbon emissions with better predictions of demand and supply. It can significantly reduce the wastage while production and transportation and recommend environmental friendly options as well.
8. Making Agriculture Smarter:
Mixed crop agriculture and energy efficient practices can be encouraged according to the status of the agricultural lands available. ML can also consider climatic conditions and predict the most productive method to grow crops as intelligent farms save not only the environment but also increase produce and finances.
9. Improve Deforestation Tracking:
Tracking and prediction of deforesting is a tedious and manual process. But, satellite imagery and computer vision combined with ML algorithms can be used to detect illegal deforestation activities.
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