Abstract
In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning (DL) algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year 2019, for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels.
Highlights
Approved energy sources are petroleum, coal, and natural gas, while the alternative green energies that will be relied upon in the future are solar and wind energy which may be important in different life fields such as medical, industrial, military, and social applications [1]
Many of the determinants were previously preventing some countries from using alternative energy, but with technological development and modern technologies, they have become available to many countries [3]. e low cost of green energy has encouraged many countries to use it as an alternative or at least an assistant to reduce energy consumption dependent on carbon fuels
The increase in the percentage of the population is reflected in the increase in the demand for energy sources, which in turn leads to an increase in global energy consumption, which leads to environmental degradation day after day [4]
Summary
Approved energy sources are petroleum, coal, and natural gas, while the alternative green energies that will be relied upon in the future are solar and wind energy which may be important in different life fields such as medical, industrial, military, and social applications [1]. Modern countries with a big part of industries use alternative energy, namely, in the USA, solar energy is considered the main source of power consumption in California, and 24% of power resources in Germany use wind turbine generators to supply the people [8]. In many interesting studies of improving energy efficiency, machine learning is used to extract useful information from big data when modeling the system managements. For normal machine learning and data mining algorithm, a comparing method is not used for feature extracted due to its simple nature, so the final prediction was not accurate For this reason, feature extraction is considered in this study to manipulate them before processing, as a preprocessing stage.
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