\documentclass[english]{SPFShortReport}
\usepackage{subfigure}
\usepackage{spfFigures}
\usepackage{longtable}
\usepackage{url}
\usepackage{gensymb}
\usepackage{booktabs}
\usepackage[yyyymmdd,hhmmss]{datetime}
\reportName{Comparison between python fit and Type977 predictions for heat pump LA_12TU}
\reportSubName{Parametric Heat Pump calculation} 
\reportDate{\today \hspace{0.1cm} at: \currenttime \hspace{0.1cm} h} 
\author{jschmidl}
\address{jschmidl}
\begin{document}
\begin{table}[!ht]
\begin{small}
\caption{Fitted coefficients for the heat pump.}
\begin{center}
\resizebox{12cm}{!} 
{
\begin{tabular}{l | c c } 
\hline
\hline
Coefficient &Description & \\ 
 & &$[kW]$\\ 
\hline
$P_{Q_{1}}$ & \emph{$1^{st}$ condenser polynomial coefficient}  & 1.7610e+01    \\ 
$P_{Q_{2}}$ & \emph{$2^{st}$ condenser polynomial coefficient}  & 1.8878e+02    \\ 
$P_{Q_{3}}$ & \emph{$3^{st}$ condenser polynomial coefficient}  & -1.1246e+02    \\ 
$P_{Q_{4}}$ & \emph{$4^{st}$ condenser polynomial coefficient}  & -3.8031e+02    \\ 
$P_{Q_{5}}$ & \emph{$5^{st}$ condenser polynomial coefficient}  & -7.6874e+02    \\ 
$P_{Q_{6}}$ & \emph{$6^{st}$ condenser polynomial coefficient}  & 3.4764e+02    \\ 
\hline
$P_{COP_{1}}$ & \emph{$1^{st}$ COP polynomial coefficient}  & -2.8874e+00    \\ 
$P_{COP_{2}}$ & \emph{$2^{st}$ COP polynomial coefficient}  & 5.0360e+01    \\ 
$P_{COP_{3}}$ & \emph{$3^{st}$ COP polynomial coefficient}  & 1.1045e+02    \\ 
$P_{COP_{4}}$ & \emph{$4^{st}$ COP polynomial coefficient}  & -1.0530e+02    \\ 
$P_{COP_{5}}$ & \emph{$5^{st}$ COP polynomial coefficient}  & -1.3776e+02    \\ 
$P_{COP_{6}}$ & \emph{$6^{st}$ COP polynomial coefficient}  & -4.6494e+02    \\ 
\hline
$\dot m_{cond}$ & 1820.00 $[kg/h]$ \\ 
$\dot m_{evap}$ & 5300.89 $[kg/h]$ \\ 
\hline
$COP_{nom}$ (A0W35)& 3.31 \\ 
$Q_{cond,nom}$ (A0W35)& 7.77 $[kW]$\\ 
$Q_{evap,nom}$ (A0W35)& 5.43 $[kW]$\\ 
$W_{comp,nom}$ (A0W35)& 2.35 $[kW]$\\ 
\hline
 $RMS_{COP}$ & $8.22e-02$ \\ 
 $RMS_{Q_{cond}}$ & $4.94e-01$ \\ 
 $RMS_{W_{comp}}$ & $1.62e-01$ \\ 
\hline
Fit model & Average Temperature\\ 
\hline
\hline
\end{tabular}
}
\label{CoefTable}
\end{center}
\end{small}
\end{table}
\begin{table}[!ht]
\begin{small}
\caption{Differences between python fit and Trnsys predicitons. Number of anaylzed data points :15}
\begin{center}
\resizebox{8cm}{!} 
{
\begin{tabular}{l | c c c } 
\hline
\hline
Type &error-$COP_{error}$ &error-$W_{comp}$ &error-$Q_{cond}$ \\ 
 &[-] &[W] &[W]\\ 
\hline
\hline
Sum &   2.1093 & 567.6698 & 6919.0170 \\ 
Avg &   0.1406 &  37.8447 & 461.2678 \\ 
Max &   0.1915 &  68.7223 & 652.1746 \\ 
\hline
\hline
\hline
\end{tabular}
}
\label{DiffTrnsysPython}
\end{center}
\end{small}
\end{table}
\end{document}
