Solar Ecology

The Solar Genome Project


The Solar Genome Project is a design collaborative working within the scope of the Solar Ecology Framework, and facilitating open source methods and information exchange for characterization of the solar resource of the globe. The Brownson Solar Collaborative (Solar Ecology Collaborative Laboratory), in collaboration with colleagues in industry and from Colorado State University, have developed low-cost solar radiometric systems that offer pathways to collect reliable, inexpensity, free solar data.

Our open source approach to the radiometric systems were motivated by the context of exponential solar PV farms for megawatt-gigawatt scale solar projects, both in the US and in the world. Our practice of maintaining an open source collaborative for project design and sharing of data, which is a unique innovation of Penn State University (PSU), aims to broker an exchange of diverse information, or “solar genomes” describing the solar resource beyond that of traditional radiometry devices, at significantly lower cost.

Currently, the Solar Genome Project is addressing two technology design tasks: Project SID (short for Solar Irradiance Detector) and Project ASE (All Seeing-Eye).

  1. Project SID (the Solar Irradiance Detector): SID systems are being designed and 3D printed to focus on single and/or multi-sensors radiometers (light measurement detectors, like a “solar thermometer”) for measuring Global Horizontal Irradiance (GHI) and/or Plane Of Array (POA) Irradiance integrated measures of irradiance.
  2. Project ASE (All-Seeing Eye): While Project SID focuses on single pyranometer devices and data loggers, the Collaborative have been developing methods for multi-pyranometer arrays and artificial intelligence/machine learning to address the industry need for component-based solar irradiance data. 
  3. Commonalities: the two projects use similar parts (processing unit and sensor type) and device calibration design concepts. 

The ASE radiometric system combines solar irradiance sensors with machine learning algorithms, aiming to answer the fundamental question tied to solar energy development, forecasting, and management of the energy-water nexus: how can one minimize risk for stakeholders who utilize the solar resource for goods and services (here, power and revenue)? We believe that in order to assess risk for the solar resource (smoothly changing and regionally, highly anisotropic and intermittent per site) one must have the right kind of physical data. While site-specific information may be available for centralized PV power generation, utility scale PV is not the key source of uncertainty and risk in the solar industry. Decentralized solar power production and the impact on buildings, electric-storage, and transportation is the unknown portfolio at this time.

The Solar Genome Project started with the All Seeing-Eye radiometric system concept, which emerged in 2013 as a research project using data from the US National Renewable Energy Laboratory’s SRRL (Dr. Vivek Shrikrishnan). Multi-pyranomter systems have been around for decades, but the approahes to process and synthesize the irradiance data was quite different from the machine learning methods used at Penn State today.

The aspects of single pyranometer systems were develop thanks to the work of Dr. Jay Ham at Colorado State University, and the early efforts of Ms. Worlasie Djemeh, now a solar professional.  

"Traditional" solar resource data sets and analyses have been limited to measures of GHI. But non-horizontal surfaces (e.g. PV arrays and vertical façades) cannot use GHI data directly, and so empirical correlations must be applied to transform GHI into POA, reflecting the anisotropy of the solar resource largely for "clear sky" conditions. However, these empirical relations have their limitations, which could be minimized by using more reliable or precise solar data. Hence the new basis of measures motivating solar risk assessment for decentralized photovoltaic (PV) power production, smart buildings, smart grids, and energy storage solutions requires Direct Normal Irradiance (DNI) in addition to GHI.

The ASE system is expected to provide the necessary solar irradiance information by being capable of characterizing the different solar irradiation components (DNI, GHI, and DHI -Diffuse Horizontal Irradiation), while the SID systems will provide reliable solar data of GHI and POA but at significant lower cost compared to similar solutions.

The development of the systems is lined by two principles: it must be affordable and reliable. We try to follow these principles by making Solar Genome Project an "open source'' project, meaning that the design of the sensors and related software will be available for free, which allows the sensors to be reproduced virtually anywhere. Also we use the Do It Yourself (DIY) concept when building our systems, and select components that are to be assembled by us in order to work. This last approach is a key to lower the cost and have customized solutions.

The systems are designed and assembled by the PSU Solar Ecology Collaborative Laboratory, composed of undergraduate and graduate students and is intended to be used for both educational and commercial purposes, being the first the focus of its development. The systems are registered under creative commons license and can be reproduced free of cost. The following links provide more details about the Solar Genome Project and about the SID and ASE systems:

Solar Genome and SID/ASE Project History

Solar Irradiance Detector (SID) System

All Seeing-Eye System

Live Data

Do-It-Yourself (Build Your Own SID/ASE System)