# Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques
## 7 Discussions and Conclusions  
In this paper, we propose a scheme to mitigate the problems caused by communication system malfunctions on MG control and management. Our work combines three different, but related, areas of research, which are MG power management, M2M communication, and DL based energy consumption and production forecastings. Previous studies that focus on load/generation forecastings for MG control and management did not deeply focus on communication aspects. On the other hand, it is challenging to find a communication-related MG publication that include DL based load/generation forecasting. That is to say, the authors could not find a similar study in the current literature that equally covers all the considered aspects of this work. Furthermore, the concept of enhancing the communication message content that transmits power references from an external EMS to an ESS in a typical grid-connected MG configuration has novelty and justifies that this proposal can empower the existing literature. For the sake of providing a definite discussion on the findings of this research several points can be noted as follows:  
* The effects of payload sizes on end-to-end communication latencies were experimentally tested. The results proved that by enlarging the messages in the scale required for this application no serious latency increases were observed. Figure 18: Two different cases of link losses. * It was proposed to use RNN-LSTM algorithms for load and PV generation forecastings and simulation results proved that although the forecastings were not always perfect but the general patterns were consistent. * The unfavorable energy interactions with the grid through the PCC in case of communication link failure was analyzed. It was shown that these deviations from internal energy balancing can be mitigated by designing a more capable communication infrastructure that can prepare, arrange, and transmit enhanced messages with forecasted power references for next time steps. This helps the ESS to anticipate its upcoming, however, not 100% accurate setpoint in the case of communication link failure. * Numerical evaluation for internal energy balance of the MG by using the proposed communication architecture and utilizing the LSTM-RNN load/generation forecasted values was conducted and the results were presented for two different communication link loss scenarios. In summary, the findings of this paper demonstrate how some detrimental effects of communication outage on the optimum operation of MGs can be minimized by enhancing message's content with the help of utilizing deep-learning methods. This is not a simple and trivial problem, therefore, future work will be required so that the concept can be further investigated. Potential future research topics will be focused on considering different MG configurations such as islanded MGs based on VSIs and also other communication protocols and infrastructures. In addition, other deep-learning based forecasting methods will be investigated and their capabilities to perform short-term or very short-term predictions will be evaluated. The aforementioned future works are just some examples and the potential of expanding the concept introduced in this paper is vast according to its originality. The authors of this manuscript, namely, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], identified by the acronyms indicated in brackets, declare the following contributions to the work. [PERSON] and [PERSON] conceptualized the work and developed the methodology. [PERSON] and [PERSON] implemented the experiments from the hardware and software point of view for the described system. [PERSON] obtained the funding and supervised the work together with [PERSON]. [PERSON] prepared the original draft with the help of [PERSON], [PERSON], [PERSON] and [PERSON]. The authors reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.