## 5 Conclusions  
The ANNs turned out to be a reliable and mature model for forecasting energy resources and the level of associated pollutant emissions. The great interest in the subject has led to the publication of research in a wide variety of prestigious scientific journals. In particular, the level of productivity of two magazines stands out: _Renewable Energy_ and _Applied Energy_, with 18% and 10%, respectively, of the total published. These two journals belong to the Elsevier publishing house. Their areas of knowledge are energy and fuel and green and sustainable science and technology and energy and fuel and chemical engineering, presenting JCR indexes and Q1 quartiles. The descriptive analysis of the journal articles together with the analysis of their content allowed for the finding of similarities regarding the use of ANN type. Both in the applications of ANN to renewable energies and in the applications of ANNs for GHG prediction, the most used ANN type was the MLP, used in 80% of the research. This article aims to provide a detailed description of the existing literature on the different applications of ANNs to the energy transition. It includes relevant aspects such as the use of renewable energies and the reduction in GHG. Additionally, the analysis of results will provide future research with data on current trends in the management of ANN and existing limitations. Although the research results have been published throughout the world and in 30 different countries, 80% of the research is concentrated in India, Turkey, Iran, USA, China, Spain, Italy, and Saudi Arabia. Especially relevant is the contribution of India with almost 20% of the total. The analyzed research began in 1996, focusing on GHG prediction and extended until 2022. The years with the highest number of publications were 2005 and 2009. In addition, the rate of publications remained constant over the years, with a significant resurgence of publications in 2020, confirming the interest in the topic. In this way, the use of ANNs presents a sufficient temporal development to demonstrate its usefulness and compete with  
Figure 12: Trend in the use of activation functions. the use of other algorithms. Some variables were difficult to model with ANN, for example, environmental variables due to the large number of associated uncertainties. However, the ANN turned out to be a valid prediction aid. Thus, ANNs occupy a central place in the transition due to their ability to learn and decide and are increasingly present in intelligent systems. As it has been shown, ANNs are very useful in the field of renewable energies for predicting wind speed, solar radiation, or wave height. This is of great importance for the design of efficient energy systems, capable of anticipating and adapting to future phenomena. Therefore, it is considered necessary that further research should reinforce efforts to develop networks capable of a wider range of predictions with a smaller number of variables. Likewise, there is also abundant literature for predicting energy consumption in buildings and traffic as well as forecasting the pollutant emissions of a given activity. Although a large number of contributions have been found in the aforementioned sectors, it would be necessary to extend the use of ANNs to all sectors that make up a country's energy scenario, such as manufacturing, construction, agriculture, and mining, among others. This is really useful as it opens the way to the development of new applications capable of predicting the energy of a complete national energy system, thus making our energy systems more competitive, safe, and sustainable. As a result, world energy systems will be in a position to meet current global environmental commitments. On the other hand, the type of architecture most chosen by the researchers was the multilayer perceptron, while the algorithms most used for training were backpropagation and Levenberg-Marquardt.