## 2 Related Work  
Since accurate load forecasting is directly related to the economies of the energy trade, in this regard, we discuss some previous load forecasting attempts in SGs as follows. In [9], the authors study the characteristics of the load time series of an SG and then compare its differences with that of a traditional power system. In addition, the authors propose a bi-level (upper and lower) short-term load prediction strategy for SGs. The lower level is a forecaster that utilizes a neural network and evolutionary algorithm. The upper level optimizes the performance of the lower level by using the differential evolution algorithm. In terms of effectiveness, the proposed bi-level prediction strategy is evaluated via real-time data of a Canadian university. This work is very effective in terms of accuracy; however, its execution time is very high. (Note: in the simulations, we have compared [12] with our proposed work. Results show that our proposed model takes \(38.50\%\) less time to execute than the work in [12]. The work in [9] adds an evolutionary algorithm-based module to the work in [12]. This means that [9] will take more time to execute than [12]. That is why we have stated the very high execution of [9].) In [10], the authors develop a DLF model that is based on a Gaussian process. The proposed predictive methodology captures the heteroscedasticity of load in an efficient manner. In addition, they overcome the computational complexity of the Gaussian process by using a \(\frac{1}{2}\) regularizer. A simulation-based study is carried out to prove the effectiveness of the proposed model. The authors have overcome the complexity of the Gaussian distribution to some extent; however, the future predictions are still highly questionable in terms of accuracy. In [11], a probabilistic approach is presented to generate the energy consumption profile of household appliances. The proposed approach takes a wide range of appliances into consideration along with a high degree of flexibility. Moreover, this approach configures the households between working days and holidays by utilizing the Gaussian distribution-based methodology. However, due to the absence of a closed form solution of the Gaussian distribution, the algorithm is very complex. Moreover, the authors assume a Gaussian distribution not only for the number of active devices in a home, but also for their power usage. These assumption are not always true, thereby making future predictions highly questionable in terms of accuracy. An artificial neural network-based short-term load forecasting method is presented in [13]. The proposed methodology is divided into four steps. Step 1 deals with the techniques of data selection. Step 2 is for wavelet transform. Step 3 is based on ANN-based forecasting. Step 4 takes into consideration the error-correcting functions. The effectiveness of the proposed methodology is verified by using practical household load demands. This algorithm has better accuracy than the aforementioned ones; however, accuracy is achieved at the cost of execution time. A stochastic model for tackling the load fluctuations of users is presented in [14], which is robust enough to predict load. This work exploits Markov chains to capture stochasticity associated with user's energy consumption in a heterogeneous environment. In other words, the authors exploit information associated with the daily activities of users to predict their future demand. In this scheme, the future predictions do not depend on past values; that not only makes it robust, but also relatively less complex, however at the cost of accuracy. A novel technique for price spike occurrence prediction is presented in [15]. This model is comprised of two modules; wavelet transform for feature selection and ANNs to predict the future price spikes. Irrelevant and redundant data are discarded from the input dataset, such that the selected inputs are fed into the probabilistic neural network-based forecaster.