# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 1 Introduction  
Modern technology in the energy sector is currently on the rise with the emergence of digital twin, smart grid, Internet of Things, big data analytics, machine learning, and artificial intelligence. These technologies have enabled the efficient utilization and management of resources and effective monitoring and control of the energy sector. However, substantial work needs to be conducted for developing countries to adopt these technologies in their energy sector. Due to various constraints on the economy and infrastructure, it is difficult for developing countries to efficiently manage the generation and distribution of power. Big data analytics is an emerging concept that aims to solve several problems currently faced in the world [1]. As the name suggests, big data can be defined as a huge dataset with great variety and is growing fast. It can be characterized based on the 7 Vs: Volume, Velocity, Variety, Variability, Versacity, Visualization, and Value. [2]. Big data analytics has the potential to accurately identify trends and concepts related to human behavior and the environment. Big data can be retrieved from various sources such as local databases and social media platforms, where an enormous amount of data is available for analysisor solving problems, such as the energy sector usage, and specifically to solve the energy crisis [3]. Energy's big data defines the system of big data technology applied to the energy sector. Additionally, energy's big data has made an immense contribution in managing huge datasets containing information about energy consumption patterns, energy demand, etc., enabling the sector to bridge the demand and supply gap [4]. Different data types, such as operational data, line data, transformer data, and load data can be collected and used together to enable the entire grid system to act intelligently. Stored data can be used to predict customer profiling, automatic demand response, efficient energy planning, and adequate pricing to enable reduced losses and conserve more energy. Big data analytics have been incorporated with smart grids to improve power distribution efficiency while optimizing energy consumption [5]. It can be divided into three mains stages: data collection, communication, and pre-processing. Data collection can be conducted using smart devices integrated into the grid, i.e., sensors, Phasor Measurement Units (PMU), smart meters, etc. [6]. The collected data are then communicated via different wireless and wired communication technologies such as Power Line Communication (PLC), WiFi, etc. [7]. The data also require a pre-processing procedure to sort, clean, and transform them into an asset for efficient use. Big data analytics can also help detect faults through an automated system that is usually impossible in conventional systems. Moreover, it can also facilitate real-time monitoring of all the consumers, hence obtaining accurate data related to power consumption patterns and eventually performing load profiling and forecasting accordingly [8]. All these features result in optimizing power consumption while reducing the demand-supply fluctuations. Power consumption has become one of the most significant concerns of developing countries, especially after the energy crises that occurred during the 1970s [9]. Considering the situation, countries worldwide are continually striving to make all possible efforts to track and optimize their power consumption. In this situation, Machine Learning (ML) and Artificial Intelligence (AI) approaches have been the biggest breakthrough that facilitate modeling, predicting, and designing an energy system that eventually optimizes power consumption. It has been established that ML and AI can be integrated into energy systems (primarily grids), where they can act smartly to reduce energy losses, improve efficiency, and collect real-time data [10].