# Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network
## 1 Introduction
Deep neural networks (DNNs) have been employed for WSNs to achieve improved networking performance [43]. Various deep learning techniques such as reinforcement learning [44] can be applied to time-series data. [PERSON] and [PERSON] developed the long short-term memory (LSTM) as a particular sort of RNN in 1997 which has a vanishing gradient drawback. The LSTM network is effective in a variety of time series learning applications, particularly nonlinear signal modeling. Statistical methods for predicting PV and wind power are often linear. They belong to a type of time-series prediction approach and are typically implemented. These models depend on using historic data to foster connections between explanatory factors in order to predict future values. The auto-regressive (AR) and moving average (MA) models, as well as their generalizations such as the auto-regressive moving average (ARMA) and auto-regressive integrated moving average (ARIMA) models, belong to the family of statistical techniques. Linear regression algorithms cannot appropriately model the non-linear relationship in the data. Among the different regression models, the KRLS algorithm enhances the fitting of anticipated signals and decreases the RMSE and MAE values. Moreover, moving to a higher dimensional space has the advantage of increasing the likelihood of correlation to a linear model and can be solved using linear algorithms. Taking this into account, the goal of this study is to look into the KRLS approach, which may provide the linear processing of nonlinear data of PV and wind power production over time. The flaws in the wireless link, particularly the \begin{table}
\begin{tabular}{l c c} \hline \hline
**Author(s)** & **RMSE/MAPE** & **Method/Algorithm** \\ \hline [PERSON]. [PERSON] et al. [22] & 2.843 & ANN \\ [PERSON] et al. [33] & 10.06 & Improved back-propagation learning ANN algorithm \\ [PERSON] et al. [23] & 2\% decrease & ANN \\ [PERSON] et al. [34] & 1.816 & LSTM network \\ [PERSON] et al. [35] & 1.36 & LSTM-RNN \\ [PERSON] et al. [36] & 8.9 & ARIMA \\ [PERSON] et al. [37] & 2.11 & Hybrid ensemble deep learning framework \\ [PERSON] et al. [38] & 17.408 & Multi-layer perceptron (MLP) model \\ [PERSON] et al. [39] & 17.45 & Combined multivariate model \\ [PERSON] et al. [40] & 0.3068 & Wavelet kernels-based (WN-LSTM) network \\ [PERSON] et al. [41] & 9.5\% improvement & Combined prediction model based on NN \\ [PERSON] et al. [42] & 1.32 & Gray correlation analysis and deep belief network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of different studies. fast fading of the channel, are one of the biggest concerns of WSN. In addition to this, the difficult aspect of the WSN is packet loss, which can lead to plant instability due to transmission latency. There is less literature available that integrated RESs with the WSN and evaluated the transmission latency. In this study, we solve the latency problem as a result of proper priority allocation among member nodes. We solve the difficulty of capturing the uncertainty of PV and wind output using the KRLS algorithm. The mathematical model of the KRLS algorithm is introduced in Section 3. After that, different ML and statistical techniques are evaluated and compared with the KRLS algorithm. The accuracy of the predicted curve is based on RMSE, MAE, and R\({}^{2}\) values. Moreover, this method keeps the advantages of linear processing, such as a fast learning algorithm and the best solution. The main contributions of this study are as:  
1. A PV and wind energy forecasting method that relies on a relatively-sized dataset of PV and wind energy collected by the data cloud center is proposed. 2. When a sensor node of WSN fails due to low link quality or a drained battery, the adopted link scheduling reconfigures the 6 TiSCH wireless network, and the network is maintained with alternate nodes. 3.