\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{CJKutf8} 
\usepackage[utf8]{inputenc}
\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}
\begin{CJK}{UTF8}{gbsn}

\title{Predicting Power flow and Wind Capacity Factor using Integrated
Spatio-Temporal Approach}



\author[1]{Swaechchha Dahal}%
\author[1]{Sambeet Mishra}%
\author[1]{Gunne  Hegglid}%
\author[2]{Bhupendra Chhetri}%
\author[3]{Thomas  Øyvang}%
\affil[1]{University of South-Eastern Norway}%
\affil[2]{Kathmandu University}%
\affil[3]{University of South-Eastern Norway - Campus Porsgrunn}%


\vspace{-1em}



  
  \date{April 25, 2026}


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





\selectlanguage{english}
\begin{abstract}
This research proposes a novel spatio-temporal approach that integrates
ConvLSTM (Convolutional Long Short-Term Memory) networks and GNNs(Graph
Neural Networks) to model and predict wind power generation and its
impact on power flow. By using ConvLSTM models, the work achieves an
R\^{}2 value of 0.977 indicating high accuracy in forecasting wind
generation dynamics across various temporal and spatial scales.
Meanwhile, the GNN model, achieving an R\^{}2 of 0.771, shows a viable
approach to modeling power grid performance without the need for
traditional iterative Newton-Raphson load flow methods. While the GNN
model could be further optimized, it does mark a substantial improvement
in scaling machine learning for real-time grid management. By
harmonizing these models, this research addresses critical gaps in
current approaches to the integration of renewable energy sources into
power grids, aligning with ongoing efforts to enhance grid reliability
and efficiency in the face of increasing renewable penetration.%
\end{abstract}%



\sloppy


\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Graphical-Abstract-IET/Graphical-Abstract-IET}

\end{center}
\end{figure}

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

