osm-meps data

Open source modeling has emerged as a transformative paradigm in energy and powersystems studies by enabling transparent, reproducible and collaborative development of tools and models.The scarcity of high-resolution input data, coupled with unlocalized modeling, poses a significant challengeto progress toward sustainable energy. This paper presents a systematic method for modeling energy andpower systems by integrating key procedural steps with first-principles thinking, the scientific method andthe engineering design process. The scientific method\u2014encompassing observation, hypothesis formulation,experimentation and analysis\u2014ensures empirical rigor, while the engineering design process\u2014centeredon problem definition, solution development and iterative evaluation\u2014prioritizes technical feasibility andscalability. The proposed systematic Open Source Modeling Method for Energy and Power Systems (OSMMEPS)was implemented to develop a solar photovoltaic (PV) energy model. High-resolution irradiance andweather data for the year 2024\u2014such as direct normal irradiance and diffuse horizontal irradiance, whichare crucial for assessing solar energy potential\u2014were sourced from the Solcast-DNV company at a spatialtemporalresolution of 90 meters and 5 minutes. The solar PV energy model yielded 1,626 MWh\/yearand was compared with an established PV energy system model (pvlib-python) which produced 1,120MWh\/year, the Global Solar Atlas (GSA) estimate of 1,513 MWh\/year and the real, site-measured energygeneration of 1,631 MWh\/year from the Serres-C solar PV plant in Greece. The OSM-MEPS RMSE is584.62 kWh and R\u00b2 is 0.887, compared to pvlib\u2019s RMSE of 1526.11 kWh and R\u00b2 of 0.232. This consistentadvantage indicates OSM-MEPS\u2019s suitability for annual-scale forecasting, particularly in systems withdiverse seasonal behavior. In Adelaide, pvlib failed to track real energy measurements altogether, with anR\u00b2 of \u20130.959. At the same time, the OSM-MEPS model captured variance with an R\u00b2 of 0.866, indicatinga strong fit for the irradiance and input physics variables. A solar rooftop PV energy feasibility case studywas conducted for the Njagu-Mhasibu-Ruiru Estate in Kenya. The OSM-MEPS model yielded 14,329.45kWh\/year, compared to pvlib\u2019s 10,048 kWh\/year and the Global Solar Atlas (GSA) estimate of 12,784kWh\/year at 8.75 kW solar PV installed capacity.