Big data analysis using intelligent statistical techniques /
Doaa Mahmoud Mohamed Abdelaty
Big data analysis using intelligent statistical techniques / تحليل البيانات الكبيرة بإستخدام التقنيات الإحصائية الذكية Doaa Mahmoud Mohamed Abdelaty ; Supervised Elhousainy Abdelbar Rady , Amal Mohamed Abdelfattah - Cairo : Doaa Mahmoud Mohamed Abdelaty , 2021 - 89 P . : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics
In the era of big data, we surrounded when in huge amounts of devices (Home Appliances) which use a very amount of electricity and this effect the community . Given the rise of smart electricity meters and the wide adoption of electricity generation technology, there is a wealth of electricity usage data available. So, The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision-making to achieve sustainable energy efficiency. The key elements for understanding and predicting household energy consumption are activities occupants perform, by clustering using of Appliances with Appliances, and Appliances with time. Appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters. Although this is challenging because it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams, it is difficult to derive accurate relationships between interval-based events, where multiple appliance usage persists, and continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. And This data represents a multivariate time series of power related variables, that in turn could be used to model and even forecast future electricity consumption
Big Data Energy Time Series LSTM
Big data analysis using intelligent statistical techniques / تحليل البيانات الكبيرة بإستخدام التقنيات الإحصائية الذكية Doaa Mahmoud Mohamed Abdelaty ; Supervised Elhousainy Abdelbar Rady , Amal Mohamed Abdelfattah - Cairo : Doaa Mahmoud Mohamed Abdelaty , 2021 - 89 P . : charts ; 30cm
Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research - Department of Statistics and Econometrics
In the era of big data, we surrounded when in huge amounts of devices (Home Appliances) which use a very amount of electricity and this effect the community . Given the rise of smart electricity meters and the wide adoption of electricity generation technology, there is a wealth of electricity usage data available. So, The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision-making to achieve sustainable energy efficiency. The key elements for understanding and predicting household energy consumption are activities occupants perform, by clustering using of Appliances with Appliances, and Appliances with time. Appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters. Although this is challenging because it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams, it is difficult to derive accurate relationships between interval-based events, where multiple appliance usage persists, and continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. And This data represents a multivariate time series of power related variables, that in turn could be used to model and even forecast future electricity consumption
Big Data Energy Time Series LSTM