Solar photovoltaic (PV) systems have drawn significant attention over the last decade. One of the most critical obstacles that must be overcome is distributed energy generation. This paper presents a comprehensive quantitative bibliometric study to identify the new trends and call attention to the evolution within the research landscape concerning the integration of solar PV in power networks. The research is based on 7146 documents that were authored between 2000–2021 and downloaded from the Web of Science database. Using an in-house bibliometric tool, Bibliometrix R-package, and the open-source tool VOSviewer we obtained bibliometric indicators, mapped the network analysis, and performed a multivariate statistical analysis. The works that were based on solar photovoltaics into power networks presented rapid growth, especially in India. The co-occurrence analysis showed that the five main clusters, classified according to dimensions and significance, are (i) power quality issues that are caused by the solar photovoltaic penetration in power networks; (ii) algorithms for energy storage, demand response, and energy management in the smart grid; (iii) optimization, techno-economic analysis, sensitivity analysis, and energy cost analysis for an optimal hybrid power system; (iv) renewable energy integration, self-consumption, energy efficiency, and sustainable development; and (v) modeling, simulation, and control of battery energy storage systems. The results revealed that researchers pay close attention to “renewable energy”, “microgrid”, “energy storage”, “optimization”, and “smart grid”, as the top five keywords in the past four years. The results also suggested that (i) power quality; (ii) voltage and frequency fluctuation problems; (iii) optimal design and energy management; and (iv) technical-economic analysis, are the most recent investigative foci that might be appraised as having the most budding research prospects.
The energy consumption of buildings presents a significant concern, which has led to a demand for building materials with better thermal performance. For this reason, determining the thermal properties of materials is essential information in the search for more energy-efficient materials. However, many time-consuming characterization experiments associated with high costs are required to ensure high accuracy and precision. Thermal conductivity (TC) is among the most relevant properties, which allows for measuring the material's heat transfer resistance. Due to the impracticality of predicting this thermal property in experimental tests, this study seeks to develop a methodology based on artificial neural networks (ANN) to predict the thermal conductivity of different types of concrete through its chemical composition. This work is broken down into two parts. The first one contains a feedforward backpropagation neural network (Multilayer Perceptron, MLP) to predict TC based on 200 experimental data sets of various types of concrete. Then, a Generative Adversarial Network (GAN) is used to expand the size of the training dataset to improve the performance of the first neural network. Currently, the model is implemented in Python, and different ANN structures varying the number of layers and neurons have been tested to find the best accuracy. The MLP model was developed using two hidden layers containing 200-100 neurons. It performed reasonably well on the training and validation dataset with an RMSE of 0.176, 0.183 W/m-K, and R² of 0.98 and 0.96, indicating a remarkable consistency between the predicted and the tested results. Furthermore, early GAN results show that it can generate data with reasonable accuracy with R² of 0.7. In the near future, we intend to increase the dataset and improve the model. Furthermore, the outcomes from this model can be helpful for the development of materials required for more energy-efficient buildings, providing quantitative information and helping the decision-makers in the construction sector.
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