With changing times, energy consumption is also changing. Machine learning systems and smart meters can drastically change energy consumption.
Fremont, CA: For decades, the government is running a drive to help consumers cut their fuel bills by regular adverts on insulating houses and energy-efficient appliances. Recently, with the help of modern technology, the government has introduced smart meters to show the consumer precisely how much energy they are using at any given point of time. However, implementing the change is difficult. The first step to effecting change is getting accurate data and building machine learning models that can understand how energy is used.
Energy companies are working relentlessly to develop products that help households save as much energy as they can. The machine learning models developed by such companies inform house owners about their energy usage and how their heating can be optimized to reduce gas. One of the challenges the smart energy companies face is building a model of utilization. Data is paramount when it comes to building a machine learning model. These smart energy companies collect terabytes of raw data every day from customers who installed smart plugs in their houses to provide detailed information on energy consumption. This granular data assists in creating a training set that is further used to build the machine learning models. However, not all customers are willing to install smart plugs. As an alternative, energy companies gather data with the help of housebuilders to add smart data devices to fuse boxes that provide granularity based on what each fuses control. The detailed data simplifies the identification of usage patterns and classes of devices.
The government requires accurate insights to change the behavior of the consumers. In the past, the government had limited success in inflicting change due to the inability to show the
difference between old energy-guzzling appliances and energy-saving appliances. Smart energy companies can show accurate data around the cost of using specific appliances. This indicates that they can inform customers about the consumption of a particular device and provide a reasonable estimate of savings by buying more energy-efficient devices.
Smart energy companies ensure that the gathered customer data is encrypted and is sent back through a VPN over the customer’s internet connection. Data securities prevent criminals to access data and use it to know when a household is unoccupied.
Machine learning systems are used for helping organizations improve their revenue and profits. It is uncommon to witness the use of these systems on helping households to save money. However, in the long-term, leveraging machine learning models will assist in reducing energy consumption which will further promote the usage of renewable energy.
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