## 3 Methodology
### Pre-Processing and Designing the Smart Grid  
Preprocessing smart grid data using IoT-based power line communication (PLC) techniques involves a series of systematic steps to transform raw data collected from various IoT devices within the smart grid infrastructure into a structured and usable format. Initially, data are collected from devices such as smart meters, voltage sensors, and weather sensors. Cleaning procedures are applied to eliminate any inaccuracies, outliers, or noise that may arise during data acquisition. Since data from IoT devices might arrive asynchronously due to the nature of power line communication, synchronization and alignment of timestamps ensure accurate time series analysis. To accommodate potential variations in scales, normalization techniques are employed. Missing data points are addressed through imputation methods, maintaining dataset integrity. Relevant features are extracted or engineered from raw measurements to enhance data quality. Aggregation over specific time intervals aids in managing data volume. The integration of data from different IoT devices furnishes a comprehensive dataset, offering a holistic understanding of grid performance. Furthermore, data transformations and labeling are applied, facilitating subsequent analyses or modeling tasks. Data security and privacy concerns are addressed through anonymization and encryption methods. Detailed documentation of each preprocessing step ensures transparency, reproducibility, and the establishment of a reliable dataset ready for advanced analytics, predictive modeling, or any intended use as shown in Figure 7.  
In the context of IoT-based PLC techniques for preprocessing smart grid data, the process extends to encompass the intricacies of power system dynamics. As data streams are harnessed from various IoT devices scattered throughout the grid, a meticulous approach to data cleaning, alignment, and transformation becomes imperative. Through careful timestamp synchronization, asynchronous data arrivals are harmonized, enabling the establishment of coherent time series datasets essential for temporal analyses. Amidst this, data compression techniques strike a balance between reducing storage demands and retaining essential information. Normalizing and scaling features cater to the diverse measurement units and scales across different devices, ensuring equitable contributions to downstream analyses. The handling of missing data through well-defined strategies averts distortions in the analytical process. Additionally, the art of feature engineering becomes pivotal, extracting insights from raw measurements, such as deriving energy consumption patterns, identifying load fluctuations, and characterizing voltage stability as shown in Figure 8.  
Our paper analyzes case studies to showcase the impact of communication and interoperability layers. These include core networks, station networks, field networks, and process interfaces. Figure 8 illustrates the architecture of these layers, facilitating seamless data exchange. The case studies demonstrate the importance of robust communication infrastructure and standardized protocols for efficient system performance.  
The convergence of data from disparate IoT devices instigates the integration of multiple data streams, granting a unified perspective into the grid's behavior. While data transformations adapt data for specific analytical paradigms, the labeling of instances anchors supervised learning tasks, enabling the identification of critical grid events. Security precautions are paramount, warranting the application of encryption and anonymization techniques to shield sensitive information.