commit 1970eaca93044bbfffdf3863435d73cf74f9bfc2
parent 93b6f20f7bd641c0403c24ac50b70a06ee19ac82
Author: Ivan Gankevich <i.gankevich@spbu.ru>
Date: Thu, 25 Mar 2021 14:17:16 +0300
Data collection section.
Diffstat:
| main.tex | | | 52 | ++++++++++++++++++++++++++++++++++++++++++++++++++++ |
1 file changed, 52 insertions(+), 0 deletions(-)
diff --git a/main.tex b/main.tex
@@ -188,9 +188,61 @@ Parameter \(c_{t,x,y,z}\) controls the shape of the autocorrelation function in
the corresponding direction; it does not have simple relationship to the wind
velocity statistical parameters.
+\subsection{Data collection}
+
+We installed anemometer on the tripod and placed it on the balcony. Then we
+connected load cells to the microcontroller via HX711 load cell amplifiers and
+programmed the microcontroller to record the output of each sensor every second
+and print it on the standard output. Then we connected the microcontroller to
+the computer via USB interface and wrote a script to collect the data coming
+from the USB and store it in the SQLite database. We decided to store raw
+sensor values in the range of 0 to 65535 to be able to calibrate anemometer
+later.
+
+Over a period of one month we collected 2.7M samples and filtered out 14\% of
+them as having too large unnatural values. We attributed these values to
+measurement errors as they spread uniformly across all the time span and are
+surrounded by the values of regular magnitude. TODO picture of a large error.
+
+From the remaning data we choose days with wind speeds above the average as
+reported by EMERCOM of Russia\footnote{https://en.mchs.gov.ru/} for Saint
+Petersburg. TODO picture of average and max speeds grouped by day. After that
+we divided each day into two-hour intervals over which we collected the
+statistics individually. From these intervals we choose the ones with data
+distributions close to unimodal, so we could easily fit them into the model.
+
+\begin{table}
+ \centering
+ \begin{tabular}{ll}
+ \toprule
+ Time span & 31 days \\
+ Size & 106 Mb \\
+ No. of samples & 2\,736\,114 \\
+ No. of samples after filtering & 2\,362\,523 \\
+ Resolution & 1 sample per second \\
+ \bottomrule
+ \end{tabular}
+ \caption{Dataset properties.\label{tab-dataset}}
+\end{table}
+
+\begin{table}
+ \centering
+ \begin{tabular}{ll}
+ \toprule
+ Load cell capacity & 1 kg \\
+ Load cell amplifier & HX711 \\
+ Microcontroller & ATmega328P \\
+ \bottomrule
+ \end{tabular}
+ \caption{Anemometer properties.\label{tab-anemometer}}
+\end{table}
+
+
\section{Results}
+\subsection{Anemometer verification}
+
\subsection{Verification of wind velocity distribution}
\subsection{Verification of wind direction distribution}
