# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 1 Introduction
Data inspection variables that represent performance measures of ANNs include processing time, accuracy, performance, latency, scalability, and fault tolerance. These elements help calculate precision values; for instance, root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of assurance (R\({}^{2}\)), and time. The DL technique has a fast execution ability in enormous implementation, necessitating extensive research in this area. From the mathematical viewpoint, DL is generally utilized because of its special ability of adaptivity, self-learning, adaptation to non-critical failure, and non-linearity in contribution to output planning [15]. The PV generation framework can be incorporated with other possible power sources, for example, a wind turbine, which enjoys the benefit of being useful constantly, in both winter and summer, with optimal production during the periods when PV is limited (generally winter and night) and vice versa. The bidirectional flow of electricity can be made possible by using ESSs to allow the extra energy to be stored during the nighttime and load demand to be met during the daytime. In comparison with systems made only of renewable energy sources, the inclusion of several energy sources in hybrid systems improves the system efficiency and dependability of the grid energy supply, lowering energy storage requirements. Various methods for modeling MG's complex setup to achieve practical, cost-effective, and environmental benefits are available in the literature [13]. The majority of extant research is focused on deterministic-based mathematical structures. Although the deterministic perspective is regarded as one of the most extensively utilizedstrategies in MG operation, the inclusion of uncertainty has grabbed the attention and interest of academics and industries. In the literature, stochastic procedures, sensitivity analysis, and fuzzy modeling approaches have all been widely utilized strategies for dealing with a variety of unpredictable natural occurrences. The incorporation of solar and wind energy systems in remote MG control is examined in [16] and demonstrates that, if properly managed, increased hybrid renewable energy penetration can be a potential solution. A previous study in [17] presented a Monte Carlo-based stochastic approach to imitate the impact of renewable energy uncertainty under a stochastic situation. The study demonstrated that, when uncertainty is taken into consideration, a realistic solution may be found that accounts for the potential ambiguities inserted in the network. In [18], a multi-objective model was created to reduce power loss and the cost of the renewable energy system. A comparable study in [19] focused on MG optimization tactics and management change effects, as well as the influence of EVs on MG operations. Models for PV or wind power production estimation have been presented in the literature [20]. Deterministic techniques, based on physical events, attempt to anticipate PV output by using software such as PVSyst and System Advisor Model considering the electrical model of the PV devices. The electrical, thermal, and optical characteristics of PV modules were utilized for the deterministic technique in [21]. In some cases, deterministic forecasting approaches fail to account for the vulnerabilities in the PV power data. Using an ensemble of deterministic forecasters is one of the most common methods giving rise to probabilistic ambiguity. The fundamental drawback of ensemble-based PV power forecasting models is the excessive computing cost, creating a real-time difficulty in practice. These methodologies may be lacking to completely extricate the nonlinear elements and static characteristics. Several researchers presented PV power output prediction models based on a single-layer feedforward ANN and recurrent neural network (RNN) [22]. With the help of meteorological data and weather classification, PV power production prediction models based on ANN were created in [23].