## 3 Methodology
### Data Collection and Analysis  
In order to evaluate the operational performance of the combined BESS and HESS, the data collection and analysis part comprises obtaining and carefully examining relevant data. This phase commences with identifying credible data sources, encompassing real-time energy consumption logs, renewable energy generation metrics, and performance indicators from the storage systems. Subsequently, the collected data undergoes processing using diverse statistical and computational methodologies to ensure precision and consistency, facilitating comprehensive analysis. Finally, specific metrics are employed to gauge the efficacy of the energy storage systems, such as energy efficiency, cost-effectiveness, reduction in peak demand, and the percentage of renewable energy utilization. This meticulous analysis is instrumental in comprehending the implications and advantages of integrating BESS and HESS within the infrastructure of the Engineering 5 Building at Kangwon National University:  
* Sources of Data: Data for this study were collected from multiple sources, including real-time energy consumption data from the Engineering 5 Building, renewable energy generation data, and performance data from the BESS and HESS. These sources provide the necessary information for accurately modeling and simulating the integrated energy storage systems. * Data Processing Methods: Collected data were processed using various statistical and computational methods to ensure accuracy and reliability. This involves filtering, normalization, and interpolation to create a consistent dataset for simulation. MATLAB's data processing capabilities are used to handle large datasets efficiently. 1. Data collection: Involved acquiring real-time (simulation) data from Kangwon National University's Samcheok campus, focusing on energy consumption patterns, renewable energy generation, grid power fluctuations, and pricing dynamics. Sensors and monitoring devices were strategically placed across the campus to ensure comprehensive data capture. These devices recorded parameters suchas electricity usage, solar power output, battery storage levels, and hydrogen production and consumption rates over a continuous 24 h period, capturing daily variations and interactions within the energy management system (EMS). 2. Data cleaning: These outliers were carefully examined to determine if they were errors or valid anomalies. Missing values were handled through imputation techniques or removed if they had negligible impact. Duplicate records were identified and eliminated to avoid skewing the analysis. 3. Data Analysis: Once cleaned, the data underwent thorough analysis using various statistical and computational tools, primarily utilizing MATLAB for simulation and modeling. The data were categorized according to the different components of the EMS, such as battery energy storage systems (BESSs), hydrogen energy storage systems (HESSs), grid power, and renewable energy sources. 1. Battery Energy and Power Analysis: Analyzed storage and discharge patterns throughout the day by calculating total energy stored and used at different times and examining power output to determine efficiency and peak usage times. 2. Grid Power and Price Analysis: Examined fluctuations in power supply and demand, analyzing the impact of external factors like market conditions and demand response strategies on grid stability and pricing dynamics. 3. Hydrogen Energy and Power Analysis: Investigated charging and discharging cycles of the HESS, analyzing power output fluctuations to determine the system's capability to manage hydrogen production and consumption effectively. 4. Load Analysis: Analyzed variations in power demand throughout the day, identifying peak demand times and assessing the effectiveness of load management strategies in balancing supply and demand. 5.