# 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 Data Management  
Big data analytics techniques are becoming a norm globally, especially in the developed countries. Therefore, power systems applications were introduced for various purposes in the energy sector. Power systems have become increasingly efficient since the concept of machine learning is integrated with power consumption. Moreover, the increased reliance on advanced infrastructure such as Smart Grid (SG) leads to the in creasing number and quality of power applications, which work collaboratively to make power consumption more efficient [24]. Typically, SGs consist of smart devices like smart meters, sensors, two-way communication channels, and advanced control systems that enable effective power management. These SGs have brought substantial advantages for the suppliers and consumers as it enables them to predict the cost of energy, load, and demand [24; 25]. Furthermore, smart meters in SG are integrated with multiple sensors to track power usage data and e-pricing details to the electricity company and conserve energy by monitoring their real-time usage. This saves a substantial amount of money for the consumers and lessens the electricity suppliers' burden, who work tirelessly to bridge the gap between energy supply and demand [26].  
Additionally, the presence of a heterogeneous environment of smart and mechanical meters adds a lot of challenge to any data management proposed system. [PERSON] et al. [15] proposed an efficient system to integrate data into heterogeneous environments based on data mining techniques. While [PERSON], [PERSON] et al. [27], proposed a method to manipulate the growing smart grid data by treating it as outlier data (a data that differs exceptionally from other observations), then categorize them into outlier rejection and outlier mining groups based on data-driven analytics and data mining techniques.  
Moreover, [PERSON] et al. [16] proposed a tensor-based big data management scheme to reduce the data divergence problem in the dataset generated from diverse meters. Likewise, [PERSON], [PERSON] et al. [28] proposed a system that can extract good quality data from a large-scale heterogeneous database environment resulting from multiple data sources. This study suggests using edge computing infrastructure and a unified data representation model for data integration. In addition, [PERSON] et al. [29] suggested using many other big data techniques and/or many other machine learning approaches for data integration purposes in heterogeneous environments. Although most of these studies presented a promising result in heterogeneous environments, they have only discussed the existence of a set of physical databases or datasets of smart meters with different data structures, and without addressing the handling of the data when a structural database is lacking such as when data are transferred or generated from mechanical meters. Therefore, a heterogeneous environment and diverse data without a digital transformation structure due to mechanical meters strongly motivate further study.