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Microgrids are emerging as feasible solutions to handle local energy systems. Several factors influence the development of such systems, such as technical, economic, social, legal, and regulatory issues. These important aspects need to be addressed to design appropriate microscale projects that take into consideration adequate technology without underestimating local characteristics. This article aims to propose a framework design for microgrid optimization using technical, social, and economic analysis. The framework is presented through a small island case study that shows each step of the method. As a contribution, this work provides a multi-objective optimization framework with different criteria consideration, such as the inhabitants’ cost of living and inter-cultural aspects, instead of traditional technical and economic analysis. The results show the applicability of the proposed framework showing better alternatives when compared with actual or future improvements in the study case scenario.

Time series forecasting is an important task in various fields of science, like economy, engineering and other areas that use historical data to predict future problems. In this context, Artificial Neural Networks have shown promising results for this task, when compared with the traditional statistical techniques. Thus, this research aims to evaluate the performance of NARX-neural network (Nonlinear Autoregressive Model with Exogenous Input) for the purpose of performing load forecasting for very short-term data from distribution substations. The cross validation was applied to evaluate different topologies. It is important to mention that the data was obtained by measures done in Brazilian substations located at two different cities. The results show the contribution of the paper once it demonstrates the efficiency of the NARXneural network compared with Feedforward and Elman neural networks, which are widely used to predict times series.Index Terms-Artificial neural networks, NARX-neural network, load forecasting.

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