Generative Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Project Code :TCMAPY1084

Objective

The primary objective of this project is to advance the accuracy of energy consumption and generation forecasts, specifically within the domain of green energy resources spanning from 2008 to 2019. We aim to achieve this by implementing performance against conventional methods such as ARMA, SARIMA, LSTM, and GRU. By doing so, we seek to provide more reliable predictions to support sustainable energy management, grid optimization, and environmentally conscious decision-making. Ultimately, the project aims to contribute to the efficient utilization of green energy sources and the reduction of carbon emissions for a greener and more sustainable future.

Abstract

In this study, we present a valuable dataset spanning from 2008 to 2019, containing crucial information about energy consumption and generation, specifically focusing on green energy sources. Our primary objective is to harness the power of machine learning, employing Recurrent Generative Adversarial Networks (RGANs), to generate energy consumption and generation forecasts. We compare the performance of three prominent algorithms: ARMA (AutoRegressive Moving Average) or SARIMA (Seasonal AutoRegressive Integrated Moving Average), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). These algorithms play a pivotal role in time series analysis and forecasting, making them ideal candidates for this energy data prediction task. By applying RGANs and these algorithms to our comprehensive dataset, we aim to improve the accuracy of energy forecasts, contributing to the advancement of sustainable and efficient energy management systems. This research has the potential to revolutionize the way we handle green energy resources, ultimately benefiting both the environment and society.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.