Olive - 1 - Description

For a long time I have been interested in relationship between sustainable energy sources, climate and terrain.  An attempt to produce a megalithic chunk of software using downloaded data was instructive but failed to produce anything useful.  Software like pet dragons acquires names and this code was called Druid.  Rather than give up on the concept I recently adopted a completely different approach.  The concept was to build some simple equipment and to take one or two short data-shots each day from my back yard.  Over a period of a year this would produce a few hundred data points which hopefully would provide enough samples to draw some conclusions but not so much that it could not be assimilated.  Because hardware was involved, the name changed to Physical Druid and the equipment was called Olive.

Olive attempts to measure solar irradiance.  The sensors are two very small horizontally mounted solar panels.   Because they are flat they have a good cosine response.  During a data-shot one of the sensors is shaded from the direct rays of the sun and therefore only receives diffuse irradiance, the other is not shaded and it's output is a combination direct and diffuse irradiance.  Combining the output of the two sensors it is possible to estimate direct normal irradiance.

The current produced by a photocell is proportional to the irradiance it receives.  A resistor is connected across each cell and the voltage across the resistor is also proportional to the irradiance.  The voltage across the resistors is only a few mV which is too small for the analogue-to-digital converter on the Arduino Nano so a TS358CD op-amp is used to get the output into the range 0 - 5 volts.  The Arduino Nano is a brilliant piece of equipment, I first used it on a small dynamometer and found it easy to use for both control and measurement and it works well with Olive.  The Nano connects to a Raspberry Pi model 2 which acts as data logger powered by a 20,000 mA power bank.  A data-shot consists of approximately 16,000 observations over an eight minute interval.  During a data-shot a photo of the sky is taken with my mobile phone.

The observing site is real-world rather than ideal.  Olive sits on pillar on the wall which separates my back yard from the street, the quality of the measurements will improve as the sun gets higher in the sky.  On the plus side, Olive is a good conversation starter.

Data from Olive is fed to an evolving suite of programmes on a Raspberry Pi 3, I'm still figuring out how to use the stuff.  The basic presentation consists of a plot of the sensor output, a cropped and resized version of the phone photo, a graphic showing the attenuation of the irradiance by any passing cloud.

To give the observations some context, some data from the Global Forecast System has been added.  The objective is to attempt to explore the results using machine learning methods.