# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 3 Related Works
### Existing and Potential Applications in Power Consumption for Load Forecasting  
A variety of applications have significantly added various beneficial features to the smart grid, hence making the system more user-friendly [30]. By predicting the parameters, consumers and suppliers can adapt their behavior to keep power consumption efficient while avoiding losses. these applications have been implemented in various areas of power consumption that bring several benefits to consumers. One of the most famous implementations is the concept of smart homes that control all the electrical appliances to enable highly efficient consumption. It also connects these electrical appliances to various sensors from where data are collected and sent to the distributors, who can then use the data for predictive analysis [22; 23; 24; 25; 26; 27; 28; 29; 30]. Smart homes are becoming increasingly popular in developed countries because they provide a high level of automation in controlling electrical appliances while ensuring that energy is not wasted by unnecessarily turning on the appliances [31]. Moreover, smart homes also allow users to track their energy usage at any time to monitor and control their energy bills. Therefore, smart homes make power consumption very efficient while reducing energy costs [24; 25; 26; 27; 28; 29; 30]. These existing and potential applications are highly effective in solving some of the energy problems, but there is still a need for more research to handle the energy problems that are more prevalent in developing countries.  
Moreover, many ML and AI algorithms have been used to develop forecasting applications, such as Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANN), Linear Regression (LR), and Fuzzy Logic (FL) [33], while forecast techniques are a complementary part of designing and operating power systems and planningin the energy sector. The forecasting techniques can be classified into three main areas, namely long-term load forecasting (LTLF) for yearly observations, medium-term load forecasting (MTLF) for monthly observations, and short-term load forecasting (STLF) for daily or weekly observations. [30; 31; 32; 33; 34]. The suitable algorithms, techniques, and observed periods for load forecasting will fully depend on the forecast horizon type and the features of the data. In this study, we focused on STLF as it is more relevant to our collected dataset volume. In Japan [17], STLF is performed using a hybrid K-means clustering and ARIMA for load forecasting for one hour ahead; the results showed high accuracy in load forecasting with the proposed method. In Indonesia [18], a hybrid methodology using linear Recurrent Neural Networks (RNN) has been proposed for short-term forecasting to overcome the shortcomings of each method. Although hybrid algorithms can give good results, the accuracy was unclear in this study.  
Aside from that, in China [3], another hybrid method with a decomposition-based quantile regression forest has been proposed, where the results show the proposed modeling can acquire the narrowest prediction intervals at various confidence values. Likewise, in India [19], a hybrid STLF using the ARIMA-SVM model has been proposed, where the results show an ideal situation, where the study was based not only on energy consumption data but also on external factors such as weather. Moreover, in the Russian Federation [35], many algorithms have been proposed, such as long short-term memory (LSTM), artificial neural networks (ANN), and support vector machine (SVM) regression for different periods. It was found that SVM regression gives 21% better accuracy in the power consumption forecasting problem, while in Argentina [36], a hybrid ARIMA and Regression Tree (RT) models have also been used for STLF, although this study relied on an interval-valued time-series dataset. The proposed models show good accuracy. The most related works are summarized in Table 1.