Samsung unveils industry’s first AI hybrid fridge using ‘Peltier’ modules

Samsung introduces industry's first AI hybrid fridge with Peltier modules for enhanced energy efficiency and performance
Samsung unveils industry’s first AI hybrid fridge using ‘Peltier’ modules
Samsung unveils industry’s first AI hybrid fridge using ‘Peltier’ modules

On Thursday, Samsung Electronics introduced the industry's inaugural hybrid refrigerator, integrating semiconductor components and artificial intelligence (AI) to enhance energy efficiency and performance.

According to Yonhap news agency, the new Bespoke AI Hybrid Refrigerator employs a hybrid cooling approach that combines Peltier modules with a conventional compressor. Peltier modules, also known as thermoelectric modules, are devices used for thermal control, capable of both heating and cooling functions to maintain precise surface temperatures.

While typically found in small-scale consumer products like portable coolers and minibar fridges due to their historically lower power efficiency, Samsung has advanced their technology to apply Peltier modules in larger appliances with improved energy efficiency.

Samsung's Hybrid Refrigerator is reportedly the first large-scale fridge to integrate Peltier modules for cooling, marking a significant advancement in appliance technology. Wee Hoon, vice president at Samsung, described the innovation as creating a hybrid cooling system akin to how hybrid cars optimize efficiency through combined power sources.

The refrigerator's hybrid cooling system effectively reduces power consumption, addressing a significant energy demand within households. During regular operation, the AI inverter compressor operates independently to maintain consistent energy use. However, during high-demand scenarios such as peak ice usage in summer or when large quantities of food are added, the Peltier module activates to swiftly and efficiently cool the interior.

Also, read: Nothing to unveil CMF Phone 1 on July 8 in India

Furthermore, an AI algorithm powered by machine learning optimizes performance by discerning between routine door openings and situations necessitating maximum cooling efficiency.

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