首页|Study Findings from Heriot-Watt University Update Knowledge in Machine Learning [Optimizing the Operation of Grid-Interactive Efficient Build ings (GEBs) Using Machine Learning]
Study Findings from Heriot-Watt University Update Knowledge in Machine Learning [Optimizing the Operation of Grid-Interactive Efficient Build ings (GEBs) Using Machine Learning]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Edinburgh, United Kingdom, by NewsRx correspondents, research stated, "The bu ilding sector constitutes 40% of global electric energy consumptio n, making it vital to address for achieving the global net-zero emissions goal b y 2050." Our news reporters obtained a quote from the research from Heriot-Watt Universit y: "This study focuses on enhancing electric load forecasting systems' performan ce and interactivity by investigating the impact of weather and building usage p arameters. Hourly electricity meter readings from a Texas university campus buil ding (2012-2015) were employed, applying pre-processing techniques and machine l earning algorithms such as linear regression, decision trees, and support vector machines using MATLAB R2023a. Exponential Gaussian Process Regression (GPR) sho wed the best performance at a one-year training data size, yielding an average n ormalized root mean square error (nRMSE) value of 0.52%, equivalent to a 0.3% reduction compared to leading methods. The developed sy stem is presented through an interactive GUI and allows for prediction of extern al factors like PV and EV integration."
Heriot-Watt UniversityEdinburghUnite d KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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