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Machine learning for energy consumption prediction and scheduling in smart buildings

Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Several approaches and models have been adopted for energy consumption prediction and scheduling.
Machine learning for energy consumption prediction and scheduling in smart buildings

Smart Grids (SG) have emerged as a solution to the increasing demand for energy worldwide. The grid refers to the traditional electrical grid that is a collection of transmission lines, substations, and other components that make sure energy is delivered from the power plant to the home or business. The smartness in the SG resides in the two-way communication between the utility and the customers, in addition to the sensing along the lines. The main components of an SG are controls, computers, automation, in addition to other new technologies that are working together to accommodate for the quick increase in the energy demand. The SG has many benefits among which we state: more efficient energy transmission, improving security, reducing peak demand which helps with the decrease of electricity rates, etc. SG is also known for the use of renewable energy sources.

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