\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Modular deep learning approach for wind farm power forecasting and wake
loss prediction}



\author[1]{Stijn Ally}%
\author[1]{Pieter-Jan Daems}%
\author[1]{Timothy Verstraeten}%
\author[1]{Ann Nowé}%
\author[1]{Jan Helsen Vub}%
\affil[1]{Vrije Universiteit Brussel}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
\textbf{Power production of offshore wind farms depends on many
parameters and is significantly affected by wake losses. Due to the
intermittency of wind power and its rapidly increasing share in the
total energy mix, accurate forecasting of wind farm power production
becomes increasingly important. This paper presents a data-driven
methodology for forecasting power production and wake losses of wind
farms, taking the dynamics of weather conditions into account. A modular
approach is adopted by integrating multiple deep neural networks,
resulting in a digital twin of the wind farm that can be interfaced with
weather forecasts of different meteorological service providers. Another
key advantage of the employed data-driven approach is its high
prediction speed compared to physics-based methods, such that it can be
employed for applications where real-time power forecasting is required.
The methodology has been applied to two large offshore wind farms
located within the Belgian-Dutch wind farm cluster in the North Sea.}%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

