

\chapter{Application}
\label{ch:Application}







\section{Test for the effectiveness of the optimization}
\label{sec:Test for the effectiveness}

\subsection{General symmetric mapping}
Although it's universally acknowledged that "machine makes no mistakes", we still want to test its accuracy. Overall the consistency of the inputs and outputs of the program need to be confirmed. Even though the model is very complicated not a input alone can judge an output, some relationships can be observed as the proof of the consistency, according to our common sense.

Here're some relation should be detected in the mapping.

\begin{itemize}
\setlength{\itemsep}{1pt}
\setlength{\parskip}{0pt}
\setlength{\parsep}{0pt}
    \item \textbf{WWR\_N - cDA\_N:} it should be positive correlation.
    \item \textbf{WWR\_S - cDA\_S:} it should be positive correlation.
    \item \textbf{Insulation Thickness - Heating Consumption:} it should be negative correlation.  
    \item etc.
\end{itemize}

We've detected all these features in the matrix, so we can say, at least the optimization is conformed to our common sense.

\subsection{Convergence}
We have to make sure the results are already optimized enough. The algorithms operates in loop, so the exit condition needs to be considered, in case of a too early break-out. The optimized process can be detected from the variation of the results.

~\\
So in the beginning, we conducted a experiment with an iteration of 100, generation size of 20, in the environment of Torino. The result show in \cref{fig:Variation_M,fig:Variation_SDT}. Among them, we can confirm that the convergence is deeply completed. So these experiment settings are proved valid.

\begin{figure}[h]
    \centering
	\includegraphics[width=\linewidth]{images/WallaceiX/WX_M.png}
	\caption[The variation of mean values]{The variation of mean values}
	\label{fig:Variation_M}
    \hfill
\end{figure}
\begin{figure}[h]
    \centering
	\includegraphics[width=\linewidth]{images/WallaceiX/WX_SDT.png}
	\caption{The variation of standard deviation}
	\label{fig:Variation_SDT}
    \hfill
\end{figure}


To evaluate the convergence, we have to analyze all the fitness values. In \cref{fig:Variation_M}, we can see the \textit{TotEnergyConsump, Cost} shrinking quickly within 10 generations. While \textit{\glsxtrshort{PPD}} converges slower, and get to the stable situation in around 20-30 generation. 

\begin{figure}[h]
    \centering
	\includegraphics[width=\linewidth]{images/WallaceiX/WX_SDD.png}
	\caption[The distribution of standard deviation]{The distribution of standard deviation. It shows 0-9 generations in red color; and 50-99 generations in the gradient from red to blue. Among them we can observe the \glsxtrshort{SD} is growing wider, resulting in a richer range of genotypes.}
	\label{fig:Variation_SDD}
    \hfill
\end{figure}

On the other hand, in \cref{fig:Variation_SDT} we can see \glsxtrshort{SD} varies in the process of iteration. The \glsxtrshort{SD} doesn't vary significantly, specially the \textit{\glsxtrshort{PPD}}. If a group of numbers, whose mean value and \glsxtrshort{SD} don't change a lot, we can predict the composition of these number are similar. And so do the genotypes, and we can say the evolution comes to an equilibrium. And in \cref{fig:Variation_SDD}, we can fell the pause of evolution. In general, 100 iterations are enough for optimizing the genes in our project.


\subsection{Non-Fungible solutions}
If all the solutions are fungible, the optimization become meaningless. Due to the de-crowding computation, usually the \glsxtrshort{GA} is keen on providing diverse solutions. However, if it constantly produce similar combinations, we can predict some mechanics are overwhelming the \glsxtrshort{GA}'s de-crowding operation, which often indicates the object has a simple answer in math, e.g. use a rope to enclose as much area as it can, the answer is the more similar the surround shape to a circle, the larger area it can contain. We can answer this question, once it has an answer, it become meaningless for optimization.
Only if the answer is hard or there's no a good-for-all answer, the multiple objects optimization algorithm makes sense.
On the other hand, the designers love to choose among diverse solution, which inspiring them with more perspectives.

~\\
The diversity of the genotypes can be easily detected by its being colored with different colors. Thankfully, we can observe that all the matrix are in kaleidoscope. So the diversity is no doubt.



\subsection{Effectiveness of the CNV}
We've conducted 2 times of the experiments, so that we could make comparisons among the controled variable. Specifically, during the second experiment, we have done 2 simulations on Torino, one is with \textit{Smart \glsxtrshort{CNV}}, while other's without any operable windows. The windows are completely shut down during the summer, in order to test how effective the smart \glsxtrshort{CNV} system is. And by the comparison we can also measure the advance of the smart \glsxtrshort{CNV} system and test its performances under different climates.

\begin{figure}[h]
    \centering
 	\includegraphics[width=0.99\linewidth]{images/tables/S2_Torino_GEN.100_Mod.png}

    \caption[Modified gen.99 CNV Comparison]{Red marks indicates the selected elite values from Gen.99 in Torino where the \glsxtrshort{CNV} is blocked on the left and activated on the right.}
    \label{fig:S2_Dx_Gen.100_Modified}
\end{figure}

~\\
In analogy, in some cases of the second simulation, we dress the cute dogs with clothing, blocking the cutaneous respiration (breathing via skin), and then observe the direction of the evolution.

\subsubsection{How to measure the effectiveness}
The two groups of dogs are bread under controlled condition. And we will measure the final performance of each group. Obtained the final data, 6 \glsxtrshort{KPI}s are extracted to make comparison. 

\begin{itemize}
\setlength{\itemsep}{1pt}
\setlength{\parskip}{0pt}
\setlength{\parsep}{0pt}
    \item General mean value of \glsxtrshort{PPD}
    \item General mean value of \glsxtrshort{EUI}
    \item General mean value of Cost
    \item Elite mean value of \glsxtrshort{PPD}
    \item Elite mean value of \glsxtrshort{EUI}
    \item Elite mean value of Cost
\end{itemize}





~\\
Mean value is selected as the key indicator as it represents the general performance of the final generation. However, due to the de-crowding mechanism inside the \glsxtrshort{NSGA2}, the individual solution tends to go extreme in one specific performance while evolving. In this case, in order to estimate the top elites' performance, the high ranking solutions need to be measured. Hence, in each fitness value, (as in \cref{fig:S2_Dx_Gen.100_Modified}) the top 5 solutions are extracted and summed up, which give the sense of the depth of the evolution trace.




\subsubsection{Comparison by CNV}

\begin{figure}[h]
    \centering
    \begin{minipage}[t]{.49\linewidth}
    \begin{figure}[H]
	\centering
	\includegraphics[width=\linewidth]{images/WallaceiX/CNV_without.png}
	\caption[Performance of the system without \glsxtrshort{CNV}]{Performance of the system without \glsxtrshort{CNV}. }
	\label{fig:CNV_without}
    \end{figure}
    \end{minipage}
    \hfill
    \begin{minipage}[t]{.49\linewidth}
    \begin{figure}[H]
	\centering
	% svg inclusion, requires inkscape
	\includegraphics[width=\linewidth]{images/WallaceiX/CNV_with.png}	
	\caption[Performance of the system with \glsxtrshort{CNV}]{Performance of the system with \glsxtrshort{CNV}. }
	\label{fig:CNV_with}
    \end{figure}
    \end{minipage}
\end{figure}



\begin{figure}[h]
    \centering
	\includegraphics[width=0.5\linewidth]{images/WallaceiX/CNV_Comparison.png}
	\caption[Ratio of CNV]{Ratio of decrement with the \glsxtrshort{CNV} system activated.}
	\label{fig:CNV_Comparison}
    \hfill
\end{figure}

In \cref{fig:CNV_with,fig:CNV_without}, EUI is in KWh, Cost is in Euro. \glsxtrshort{CNV} system globally benefits all the indicators. Also shown in \cref{fig:CNV_Comparison}, \glsxtrshort{CNV} enhances the performances generally and also creates more diversity by benefiting the elites solutions. Furthermore, it actually present more pragmatic for designers.Since where without the \glsxtrshort{CNV}, in order to pursuit some Fitness Values, the solutions occur with extreme low \glsxtrshort{WWR}. Those solutions are impossible to go deeper otherwise it become a prison. When the \glsxtrshort{CNV} is introduced, the problem solved. Technically, it enhances the importance of the windows, so the solutions with larger \glsxtrshort{WWR} prevail. With a larger windows, the solutions look more like the human designs, helping to a better inspiration on designers.






\begin{comment}

\section{Background Information}
In this section, we provide essential background information that encompasses:

\subsection{Climate of Turin City}
\subsection{Micro-Climate of the District Scale}
\subsection{Brief Information About the Building Being Retrofitted}

\section{Free-Running Simulation with Basic Needs of the EU Regulation}
This section details the free-running simulation process, including:

\subsection{Importing the Honeybee Model}
\subsection{Input Data}
\subsection{Results}

\section{Simulated Optimization Program}
Here, we present the parameters and outcomes of the simulated optimization program, including:

\subsection{Generation Size \& Population}
\subsection{Results: The Pareto-Front Input \& Output}

\section{Result Visualization}
This section focuses on visualizing the results, covering:

\subsection{Comparison: Before vs. After, Basic Regulation vs. Optimized}
\subsection{Optimization in Comfort Aspect}
\subsection{Optimization in Energy Efficiency}
\subsection{Optimization in Economic Aspect}

\section{Introduction}
In this section, we provide an introductory context that includes:

\subsection{Cost Assumption}
\subsubsection{Extensive Field for Market Analysis}
\subsection{Orientation and Context Assumption}
\subsection{General Info of 2 Simulations}
\subsubsection{Convergence and Mean Values}
\subsubsection{A Torino Pareto Front 3D Chart}
\subsubsection{Selections of the Cities}
\subsubsection{The Repository}
\subsection{Visualization}








\end{comment}







\input{content/chapters/S_Cities/Oslo}
\input{content/chapters/S_Cities/Kiev}
\input{content/chapters/S_Cities/Roma}
\input{content/chapters/S_Cities/Palermo}
\input{content/chapters/S_Cities/Geneva}
\input{content/chapters/S_Cities/Torino}



\input{content/chapters/S_Cities/Canton}
\input{content/chapters/S_Cities/Hongkong}

\newpage
\section{Evaluation of the method}
We assess the whole methodology here, to discover in which environment the method perform the best, which means it successfully find out some balance and advance combination of genetypes.

\subsection{Comparison of the similar climates}
As presented in \cref{sec:Boundary Conditions}, we selected the cities in purpose. That's to realize the comparisons. As Torino is playing the key character, so we have massive cites in the Cfa climate. And the others can also be compared in pairs. E.g. 
\begin{itemize}
	\setlength{\itemsep}{1pt}
	\setlength{\parskip}{0pt}
	\setlength{\parsep}{0pt}
	\item \textbf{Canton - Hongkong} are 2 cities closed to each others, the only difference is the distance from the ocean;
	\item \textbf{Oslo - Kiev} are all Dfb, but Kiev is in the middle of the continent while Oslo is facing Atlantic Ocean;
	\item \textbf{Kyoto - Busan} are all Cfa, the difference is the distance from the ocean;
	\item \textbf{Roma - Palermo} are all Cfa, the difference is the distance from the ocean;
	\item \textbf{Torino - Geneva} are all Cfa, and in the similar latitudes, the difference is Torino is on the southern foot of the Alps, while Geneva is on the north. So it's a chance to explore how a huge mountain affects the miditerraneo climate. But also Geneva is surrounded by the lake, while Torino is enclosed by the Alps.
\end{itemize}

Focusing on Torino, the optimized solutions are similar to those in Geneva, but the roof, louvers count, delta are different. So we can make a assumption that the solar light is stronger in Geneva with a stronger wind in summer. For Torino, it is harder to get heated up in winter; and in the summer, due to the calmness of the wind, once the heat is accumlated, it's also hard to disperse. This can be verify by \cref{Torino: wind rose}, to see the calm hours comparing to \cref{Geneva: wind rose}.

~\\
It means the CNV works more effectively in Geneva. A more dedicated schedule should be considered in Torino, since it's lacking of the wind. For Torino, the passive design should think about more to the sun rather to the wind.


\subsection{Same Method Behaves Differently in Different Climates}

Comparing the result from Torino and from Oslo, we can discover that the 3-40 cm is suitable for Torino, but insufficient to the Oslo. Nevertheless, the program provides various \glsxtrshort{WWR} genes in other city, however in Oslo, the WWR\_N are extremely monotonous. Maybe our design method is incapable to deal with the nordic weather.



\section{Experience gained from the Results}

An interesting phenomenon is observed, there're 4 cities evolve to have as less as the thickness of insulation. They're Roma, Busan, Kyoto and Tientsin\footnote{Tientsin is slightly better where it has nearly a half of genes to have a 20 cm roof.}. The end game matrix reveals that all the genes of thickness are in blues color, indicating them the monotonous lowest numbers. 

~\\
This is very interesting that they're not from the same climate class. And the program work perfectly when it's applied on Geneva, Torino and Palermo, which are also from Cfa. 
Here I have a hypothesis, the program is robust, but it's specialized in the "hotter Cfa" or "inland Cfa". Once it's applied on the city that's cold enough and ashore, it suggests the monotonous lowest insulation.

\subsection{Before vs after: Controlled natural ventilation}
The CNV only works effectively in some cities. Thankfully, it works for Torino and Geneva reducing the summer load greatly, even though Torino is lacking of the winds. However, for those cities truely hot in summer, e.g. Canton, Hongkong or Roma. This system dosen't lighten the load.




%\subsection{The smarter schedule}

\subsection{Typical strategies for \glsxtrshort{EUI}}
At least we can summarize some techniques to obtain a great performance in \glsxtrshort{EUI}. The program successfully works in Palermo, Geneva, Torino and Roma, where the \glsxtrshort{EUI} cover a large range and diverse genes are tested.

~\\
In Roma, Torino and Geneva, the key is to have a better glazing type. The matrix of these two reveal the column of glazing type and column of \glsxtrshort{EUI} are changing within the same pace.

~\\
In Palermo, the louvers count plays the important role but it's without the support from the transition solutions\footnote{The louvers counts are either 9, 6 or 0. We don't know how the others perform}. In Geneva, the louvers work together with the glazing type.

\section{As real cases}
I've been living in Canton and Torino, so I use my personal experience to exam whether the selected solutions are created as the realistic design.

\paragraph{In Canton}
I selected Canton.\glsxtrshort{Gen}99 indiv.12 as the reference.\\
My grandparents live in the 7th floor which is the top of the building. So I know the most concern of the top floor. During the afternoon and early evening, the envelope releases massive 
accumulated thermal. So on the roof top we need a lot of insulation, and also for the west facade. Since our program doesn't consider so detail as different construction for different orientations, I think the insulation Canton.\glsxtrshort{Gen}99 indiv.12 passes the exam.
~\\
The transparent envelope is also outstanding for its protection from the sun.
~\\
The delta is irrational, however comparing to the nearby solution, maybe it makes little difference.

\paragraph{In Torino}
I selected Torino.\glsxtrshort{Gen}99 indiv.01 as the reference.\\
It seems everything is very rational to me, just as the building we can see on the Via Nizza. 
Especially, it has 2 equal \glsxtrshort{WWR} for 2 directions, accelerating the convection. So even the \glsxtrshort{WWR} is not half as the nearby solutions but the summer load reduction is as significant as the best.





%\section{A Future Turin}
%This section delves into considerations for future scenarios, encompassing:

%\subsection{Assumption of the Climate}
%\subsection{Goals of the Future Simulation: Comfort Metric, EI}
%\subsection{A New Method Considering Climate Change, Assuming Linear Climate Change}

