首页|Investigators at Xi’an University of Architecture and Technology Report Findings in Machine Learning (Enhancing Typical Meteorological Year Generation for Diver se Energy Systems: a Hybrid Sandia-machine Learning Approach)
Investigators at Xi’an University of Architecture and Technology Report Findings in Machine Learning (Enhancing Typical Meteorological Year Generation for Diver se Energy Systems: a Hybrid Sandia-machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news originating from Shaanxi, People’s Republic of China, by NewsRx correspondents, research stated, “Accurate performance asses sment of energy systems heavily relies on Typical Meteorological Year (TMY) data . The Sandia method, commonly used for TMY generation, is limited by default wei ghting criteria for meteorological parameters, restricting its suitability for d iverse energy system analyses.” Funders for this research include National Natural Science Foundation of China ( NSFC), National Key R &D Program of China.
ShaanxiPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningXi’an University of Archite cture and Technology
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