首页|Data on Machine Learning Described by a Researcher at University of Science and Technology Houari Boumediene (USTHB) (Forecasting energy demand and efficiency i n a smart home environment through advanced ensemble model: Stacking and voting)
Data on Machine Learning Described by a Researcher at University of Science and Technology Houari Boumediene (USTHB) (Forecasting energy demand and efficiency i n a smart home environment through advanced ensemble model: Stacking and voting)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Algiers , Algeria, by NewsRx correspondents, research stated, “Smart homes integrate sev eral sensors to facilitate information exchange and the execution of tasks.” Our news journalists obtained a quote from the research from University of Scien ce and Technology Houari Boumediene (USTHB): “In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely m onitor the devices of a user’s household. The present study employs a machine le arning methodology to perform a global analysis of energy consumption and effici ency in smart homes. In This work we propose two advanced ensemble models to imp rove the performance of energy consumption in smart homes, the first one is a vo ting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and e Xtreme Gradient Boosting (XGB).”
University of Science and Technology Hou ari Boumediene (USTHB)AlgiersAlgeriaAfricaCyborgsEmerging TechnologiesMachine Learning
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