首页|Researcher at Moroccan Foundation for Advanced Science Has Published New Study F indings on Machine Learning (Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis)
Researcher at Moroccan Foundation for Advanced Science Has Published New Study F indings on Machine Learning (Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis)
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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 originating from the Mor occan Foundation for Advanced Science by NewsRx correspondents, research stated, "Energy management systems allow the Smart Grids industry to track, improve, an d regulate energy use."Financial supporters for this research include Institut De Recherche En Energie Solaire Et 'energies Nouvelles; The Portuguese National Funds Through Fct, Funda ccao Para A Ciencia E A Tecnologia. The news correspondents obtained a quote from the research from Moroccan Foundat ion for Advanced Science: "Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning util ity offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techn iques for projecting energy short-term load forecasts is provided in this articl e. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critica lly analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, th is paper also proposes a data augmentation technique based on Generative Adversa rial Networks. The results show that the Deep Learning-hybrid model is more accu rate than traditional statistical methods and basic Machine Learning procedures. "
Moroccan Foundation for Advanced ScienceCyborgsEmerging TechnologiesMachine Learning
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