Prediction of Future State Based on Up-To-Date Information of Green Development Using Algorithm of Deep Neural Network

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.

Application of Deep Learning Model in Building Energy Consumption Prediction

“…Chen et al linked power consumption estimation with user behavior analysis to provide a new data mining process after considering the form of home air conditioning system and the actual usage behavior of users [ 13 ]. In order to achieve the purpose of home appliances and diagnose problems, Sun et al combine the influence of home appliances and electricity consumption, and the workflow of applying data mining includes four stages of data distribution and conversion, data analysis, data mining, and data mining [ 14 ]. The Hong Kong Polytechnic University research team compiled a data mining workflow including data preprocessing, grouping analysis, correlation analysis, and postprocessing based on the Hong Kong International Financial Center building automation system data and existing performance laboratory research—machining and application knowledge to understand the benefits of improved diagnostics.…”

Spatial and Temporal Evolution of Coupling and Coordination between Scientific and Technological Innovation and Green Economy in Anhui Province

“…In a 2019 report, the government of Anhui clearly stated that it would "promote the building of the beautiful Yangtze River (Anhui) and Huai River green economic belts and integrate eco-environmental conservation and economic construction". With increased government input into environmental conservation, the ecological environment has improved, but not sufficiently [7,8]. STI can help reduce carbon emissions, and a lowcarbon economy is conducive to both the environmental protection and economic development, thus accelerating GE development [9,10].…”