# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 1 Introduction
During transient periods, the system utilized horizon-one nonlinear finite control set MPC to steer the system towards the intended reference [22]. On the other hand, S-MPC performance is highly vulnerable to model mismatch. In other words, it must select a suitable system model. Furthermore, the increased complexity of S-MPC impacts the stability and maintainability of MG control [23; 24]. These challenges lead to accuracy issues with S-MPC methods. In addition, the computational time of S-MPC is much longer, because of the prediction horizon and various steps. Many authors have studied machine learning (ML) techniques to increase the accuracy of MG systems. When improving the scheduling effectiveness in networked microgrids (NMGs), the main goal is minimizing the effects of electricity outages.This paper presents a framework consisting of three stages, to evaluate power transactions, manage renewable energy and market price risks, and tackle uncertainties. This framework is formulated as a mixed-integer linear programming problem [25; 26; 27; 28]. On the other hand, ref. [29] introduced a novel approach utilizing the Internet of things (IoT) to dynamically optimize and regulate power loads in citizen energy communities. This technique is compared to the conventional direct load control (DLC) method. This technique aims to enhance power use efficiency using programmable appliances and dynamic demand response. In order to model the behavior of RESs, such as wind and solar, auto-regressive moving-average (ARMA)-based scenario generation was implemented. Large industries could receive direct assistance from storage and demand-side management systems, to reduce energy costs [30]. The other work employed an ARMA model to forecast solar PV, wind power generation, and electricity demand. Second, an optimal generation scheduling procedure is intended to reduce system operating expenses. The simulation results indicated that the optimal generation scheduling could minimize operating expenses under the worst-case scenario [31]. In [32], combining two models, the ARMA and nonlinear auto regressive with exogenous input (NARX), a novel method was presented for predicting solar radiation. The decision was made to utilize the benefits of both models, to produce more accurate prediction results. The simulation results validated this hybrid model's ability to predict weekly solar radiation averages. Although the previous solar radiation forecasting techniques, particularly ARMA models, are effective for particular uses, they are unsuitable for other uses requiring a high forecasting precision. Several researchers have proposed hybrid models to improve the precision of solar radiation forecasting. Moreover, there is still not a proper plant model or prediction horizon, so the computational time of the model is still to high [32]. There have been numerous studies on using ML methods in place of AR models. For instance, ref. [33] thoroughly investigated the prediction performance of several recurrent neural network (RNNs) designs, such as a long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM. Using local weather forecasts and historical weather data, ref. [34] proposed a LSTM-based next-day forecasting model of hourly global horizontal irradiance (GHI). Refs. [35; 36] suggested LSTM-based models using only the next day's weather forecast as input. The studies by [37; 38] used similar LSTM-based techniques. Refs. [39; 40] validated the performance of hybrid deep learning models built on convolutional neural networks (CNNs) and LSTM for day-ahead GHI forecasting. In addition to RNN-based approaches, there have been studies evaluating the performance of other statistical and ML models for solar irradiance forecasting, such as coupled AR and a dynamic system by [41], a Markov switch model [42], and a support vector machine (SVM) by [43]. Ref. [44] reported an LSTM-based model for hour-ahead solar irradiance forecasting.