Automated Extraction, Simulation, and Smoothing of Load Diagrams in Power Systems Using JSON Data

In power systems, accurate load forecasting and analysis are essential for ensuring reliability and efficiency. This paper presents a novel approach to processing, simulating, and smoothing load diagrams using JSON data files. We developed a Python-based script that automates the extraction of time- value pairs from complex JSON structures, representing load diagrams. The extracted data undergoes a simulation process to generate random but realistic variations, enhancing the robustness of load forecasting models. Additionally, we implement exponential smoothing to refine the simulated data, providing a more accurate representation of load trends. Our methodology offers a comprehensive toolset for power system analysts, enabling enhanced data analysis, improved load predictions, and better-informed decision-making in power system operations.