Machine Learning Based Power Utilization Prediction - Kumar, M.Aravind;Devi, T.Durga;Madhuri, G.Phani
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This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimension and time series. At the same time, there are many forms of missing electric power data, some are missing dispersedly, and some are missing in succession. Therefore, combinations of prediction methods are receiving increasing attention. We performed exploratory data analysis, pre-processing, and train- test split before training the…mehr

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Produktbeschreibung
This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimension and time series. At the same time, there are many forms of missing electric power data, some are missing dispersedly, and some are missing in succession. Therefore, combinations of prediction methods are receiving increasing attention. We performed exploratory data analysis, pre-processing, and train- test split before training the model. We used various metrics to test the advantages of the proposed model: mean absolute error, mean squared error, and root mean squared error.
Autorenporträt
El Dr. M. Aravind Kumar obtuvo su licenciatura en ECE, su máster en diseño de sistemas VLSI en JNTUH y su doctorado en la Universidad GITAM de Visakhapatnam. Trabaja como director en el INSTITUTO DE CIENCIA E INGENIERÍA DE GODAVARI OCCIDENTAL. Tiene 15 años de experiencia docente. Es miembro vitalicio de FIE, ISTE, TETE, SCIEI, UACEE e IAENG.