首页|New Machine Learning Study Results from International Hellenic University Descri bed (Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models)
New Machine Learning Study Results from International Hellenic University Descri bed (Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news originating from Thessaloniki, Greece, by NewsRx correspondents, research stated, "The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions." The news reporters obtained a quote from the research from International Helleni c University: "To attain this goal, a projection for domestic energy usage is ne eded. This work optimizes short-term load forecasting (STLF) in the building sec tor while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and ti me steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance asse ssment showed that models like histogram gradient-boosting regression (HGBR), li ght gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model per formance was reported using R2, root mean square error (RMSE), coefficient of va riation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), an d execution time."
International Hellenic UniversityThess alonikiGreeceEuropeCyborgsEmerging TechnologiesMachine Learning
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