Two approaches for synthesizing scalable residential energy consumption data

Abstract

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Applying Scikit-Learn of Machine Learning To Predict Consumed Energy in Al-Khwarizmi College of Engineering, Baghdad, Iraq

“…Gradient descent and backpropagation are two typical techniques for this After propagating the input data through its layers, the neural network model produces the predicted results. This might be beneficial for a variety of purposes, including estimating power use, saving electricity and energy, identifying appliances, projecting costs, and other energyrelated operations [36][37][38]. All of the model's parameters will be displayed in Table 3.…”

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

“…Currently, two densification methods are available in the platform -multiplication (replicating n times the dataset) and interpolation (constructing new data points based on interpolating previous intervals n times). However, more advanced methods can be implemented, such as regression-based and probability-based methods [33].…”

Synthetic data generation for tabular health records: A systematic review

“…While not containing any data from the original set SD is generated from a model that fits to a real data set. Research on this technology has been ongoing for some time with promising results in different application domains, including healthcare [9], biometrics [10] and energy consumption [11], and the need for a robust solution to capitalise on advances in Big Data and AI technology has never been greater. Moreover, a recent publication reports cases of re-identification in anonymised individual-level data shared in the COVID-19 context, leading to a reduction of critical information sharing.…”

“…Moreover, a recent publication reports cases of re-identification in anonymised individual-level data shared in the COVID-19 context, leading to a reduction of critical information sharing. This study proposes the use of synthetic tabular data generation (STDG) to enable access to useful information whilst ensuring privacy [11].…”