Abstract: Accurate mid-term load forecasting at the building level is vital for the strategic planning, operation, and sustainability of modern power systems. Machine learning approaches often require ...
The smart grid paradigm has introduced new capabilities for monitoring and managing intelligent energy systems. In this context, IoT environments integrate smart sensors and devices to record ...
A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...
Abstract: The increased use of renewable energy (RE) in residential settings is driving the demand for smart energy management systems (EMS) to optimally coordinate the purchasing and selling of power ...
1 Department of Electrical/Electronic Engineering, NAHPI, University of Bamenda, Bamenda, Cameroon. 2 Electrical Engineering, Mechatronics, and Signal Processing Laboratory, ENSPY, UY-I, Yaounde, ...
Google is testing a machine learning-powered tech in the U.S. to determine the age of users and filter content across all its products accordingly. The company said it will consider data from Google ...
Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...
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