首页|New Machine Learning Findings from University of Southern California (USC) Outli ned (A Machine Learning Framework To Estimate Residential Electricity Demand Bas ed On Smart Meter Electricity, Climate, Building Characteristics, and Socioecono mic ...)
New Machine Learning Findings from University of Southern California (USC) Outli ned (A Machine Learning Framework To Estimate Residential Electricity Demand Bas ed On Smart Meter Electricity, Climate, Building Characteristics, and Socioecono mic ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news originating from Los Angeles, California, by NewsR x correspondents, research stated, “Due to the substantial portion of total elec tricity use attributed to the residential sector and projected rises in demand, anticipating future energy needs in the context of a warming climate will be ess ential to maintain grid reliability and plan for future infrastructure investmen ts. Machine learning has become a popular tool for forecasting residential elect ricity demand, but previous studies have been limited by lack of access to high spatiotemporal resolution at a regional scale, which reduces a model's ability to capture the relationship between electricity and its driving factors.”
Los AngelesCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Southern California (USC)