The goal here is to show how to query, analyze and visualize ERA-Interim meteorological data in NetCDF format using tidy principles in R.

This is also a follow up to the AWEA Wind Resource Working Group’s webinar on the open-source ecosystem and an expansion of my original blog post: [https://renewable-analytics.netlify.com/2018/05/30/example-wind-resource-assessment-using-r/].

So, alot is made of the functionality of PVSYST as a standard tool to model PV systems. No arguments here. That said, it does not have a programming interface. Thankfully there are some great alternatives out there developed through Sandia National Laboratories, particularly the PV-LIB Toolbox (https://pvpmc.sandia.gov/applications/pv_lib-toolbox/), which contains some really excellent functions for modeling solar position, irradiance, and PV systems. The tool was initially created in MATLAB and…

I spent some time creating a shiny app in R which allows users to access monthly energy production data from power plants in the U.S. which report to the Energy Information Administration (www.eia.gov). The app is driven via a Digital Ocean droplet.

I spent some time creating a Shiny App which interfaces with the National Climatic Data Center using the excellent rnoaa R-package. Not everything in the app works perfectly but it does some useful things. The app is driven via a Digital Ocean droplet.

The purpose of this post is to illustrate different filtering techniques for calculating the actual power curve of a wind turbine using SCADA data. Typically data in which the turbine is experiencing downtime can be filtered out using status/fault/error codes provided by the Original Equipment Manufacturer’s (OEM) SCADA system. However, it has been my experience that these data are not of sufficient quality to properly filter out all ‘bad’ data and in many cases when analyzing a wind plant these…