# Smart Grid Data Management in a Heterogeneous Environment with a Hybrid Load Forecasting Model
## 1 Introduction
AI can be integrated into different electrical grid units, such as generation, distribution, and consumption, to collect data that are later used to make automated decisions without human support [11]. While highlighting the significance of ML, [PERSON] and [PERSON] [12] stated that forecasting power consumption is one of the essential tasks that offer intelligence to utilities and facilitates them in bringing improvements to the system's performance. All these tasks are only possible through the implementation of ML. Based on this evidence, it can be affirmed that ML has a great role in optimizing power consumption, specifically through forecasting. However, AI technology facilitates solving different power system problems by forecasting, scheduling, planning, and controlling using the stored data. The selection of an appropriate algorithm is determined by several factors, including the nature of the data, homogeneity, and features. This paper proposes an effective power consumption data management system for a hybrid mechanical and smart meter environment, using the Iraqi energy sector as a case study. The proposed system, abbreviated as PIAS, offers an effective methodology for data gathering and pre-processing. It will create an efficient database that can perform different types of functionalities. It also includes data analytic capability, which can increase productivity and data control in the energy sector. The main contributions of this proposed system are highlighted as follows:  
* The Capability of Heterogeneous Data Acquisition and Data Federation  
In developing countries such as Iraq, there is a state of instability and unpredictability due to a huge gap between reality (mechanical meters) and ambition (smart meters) in terms of shifting cost and time in addition to logistical difficulties. Additionally, data uncertainty results from decades of manual meter reading [13; 14]. This situation continues to the time of this study, so embarking on any transformation process will take years, especially with 5 million subscribers that are constantly growing. The need to build an efficient system capable of dealing with the current situation (mechanical meters) and prospective (smart meters) has emerged. The literature evaluated suggested employing data mining approaches to integrate data from heterogeneous surroundings [15], while another researcher developed a tensor-based big data management scheme to reduce data divergence in datasets obtained from various meters [16]. Despite the fact that the majority of these researchers showed promising suggestions for data management in heterogeneous environments, they assumed the existence of a set of physical databases or datasets without suggestions on how the data coming from the mechanical meters can be processed. The suggested PIAS system in current research is designed to address this challenge, where it acts as a data collector or data acquisition point in such heterogeneous environments. The system is designed to handle data from various sources, such as offline data (sources mechanical meters) and real-time data (smart meters) through API Gateway; moreover, the proposed PIAS is designed to offer multi-structured data in a unified database scheme. The federation of data obtained from different structures can enhance the proposed scheme and unstructured data, which are heterogeneous. This type of federation can be used to create correlations and dependencies among variables that allow the data to become dynamic visualization, informative, and comprehensible. * A Hybrid Load Forecasting Model (Clustering, Historical Data, and External Factors)  
The literature that has been reviewed suggested a hybrid load forecasting model by applying historical data analytics and/or clustering such as Nepal et al., Japan [17] and [PERSON] et al., Indonesia [18], or historical data with/without combination with external factors such weather conditions, such as [PERSON], [PERSON] et al., China [3] and [PERSON] et al., India [19].