They describe (quasi-real) time series for wind power generation \(W(t)\), solar power generation \(S(t)\) and load \(L(t)\) in Great Britain (GB), Germany (DE) and Europe (EU). The time step is \(1\e{h}\) and the time series are several years long.
\begin{enumerate}[(a)]
\item Check that the wind and solar time series are normalized to 'per-unit of installed \mbox{capacity}', and that the load time series is normalized to MW.
\item For all three regions, calculate the maximum, mean, and variance of the time series.
\item For all three regions, plot the time series \(W(t)\),
\(S(t)\),
\(L(t)\) for a winter month (January) and a summer month (July).
\item For all three regions, plot the duration curve for \(W(t)\), \(S(t)\), \(L(t)\).
\item For all three regions, plot the probability density function of \(W(t)\), \(S(t)\), \(L(t)\).
\item Apply a (Fast) Fourier Transform to the the three time series $X \in W(t), S(t), L(t)$:
\begin{equation*}
\tilde{X}(\omega) = \int_0^T X(t) e^{\i\omega t}\,\ud t \, .
\end{equation*}
For all three regions, plot the energy spectrum
$\left| \tilde{\Delta}(\omega)\right|^2$ as a function of
$\omega$. Discuss the relationship of these results with the
findings obtained in (b)-(e).
\item Normalize the time series to one, so that \(\expect{W}=\expect{S}=\expect{L}=1\).
Now, for all three regions, plot the mismatch time series
zooms through most basic general python control structures (only
brush over it and stop reading early, i.e. if you read the word
\verb+classes+ you already know too much).
\item\href{http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-2-Numpy.ipynb}{Lecture~2} is the most important and closely connected to the exercises.
\item You might as well stop now, but if you \emph{are} hooked, I recommend \href{http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-3-Scipy.ipynb}{Lecture~3} for more physics and \href{http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb}{Lecture~4} for prettier graphs.
\end{itemize}
Further reference material of help is:
\begin{itemize}
\item The website-books \url{http://python-course.eu/} (english), \url{http://python-kurs.eu/} (german); especially if you only \emph{very} quickly skim over the \href{http://www.python-course.eu/course.php}{python2 tutorial} and switch over to the \href{http://www.python-course.eu/numerical_programming.php}{numerical python} stuff early; especially of interest might be the \href{http://www.python-course.eu/pandas.php}{pandas} bit in the end, which will make the exercises a breeze at the expense of yet another package to learn.
\item the exhaustive (overly so) official python tutorial\footnote{\url{https://docs.python.org/2/tutorial/}} available in \href{https://docs.python.org/2/tutorial/}{english} and \href{https://py-tutorial-de.readthedocs.org/de/python-3.3/index.html}{german}; which will NOT introduce you to numpy or scipy.
"The following data are made available to you on the __[coures homepage](https://nworbmot.org/courses/complex_renewable_energy_networks/)__:\n",
"\n",
"`de_data.csv`, `gb_data.csv`, `eu_data.csv`\n",
"and alternatively\n",
"`wind.csv`, `solar.csv`, `load.csv`\n",
"\n",
"They describe (quasi-real) time series for wind power generation $W(t)$, solar power generation $S(t)$ and load $L(t)$ in Great Britain (GB), Germany (DE) and Europe (EU). The time step is 1 h and the time series are several years long.\n",
"\n",
"> Remark: In this example notebook, we only look at Germany and the EU, Great Britain works in exactly the same way."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"**Read Data**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"de = pd.read_csv('tutorial_data/de_data.csv', parse_dates=True, index_col=0)\n",
