A Low-Cost IoT Approach to Real-Time Cloud Motion Detection
Abstract
As solar energy accounts for a larger portion of power grids around the world, it becomes necessary to mitigate the power output variability caused by intermittent cloud cover. When applied to a photovoltaic (PV) array, this variability limits the percentage of energy in a power grid generated by solar power. This limitation applies to both grid tied and islanded power systems. Many strategies exist to mitigate these effects, including the use of backup generators but efficient hybrid solar power systems require accurate short-term forecasting of sharp changes to ground horizontal irradiance (GHI) to minimize fuel usage. This work describes an Internet of Things (IoT) network of inexpensive nodes equipped with pyranometers and explores a simplex optimization method of calculating cloud motion vectors (CMVs). The IoT network was successful in reliably measuring GHI but limited by the chosen communication modules. The simplex optimization method was found to be comparably accurate and marginally more stable in calculating CMVs when compared to the more commonly utilized Most Correlated Pair method.
Keywords
Internet of things, Solar power, Irradiance forecasting, Distributed sensor network