psychrometrics with Matlab

This is the second tutorial on climate visualization using Matlab, particularly on plotting weather data on a psychrometric chart.

Although architects are most familiar with psychrometric chart to understand comfort zone and identify appropriate passive design strategies, psychrometrics is more commonly used by mechanical engineers to study air and water vapor mixture for active mechanical devices to condition air.

Tools like Autodesk’s Ecotect Weather Tool or Climate Consultant are typically sufficient for architects to study comfort zone and passive design strategies. However, for active applications, we need to be able to enter our own data points, such as ones that come from mechanical specs or material properties.

4 scripts 2 data files are included in this distribution.

Make sure all 6 files are mapped into your Matlab path. Easiest way is to put all the files into your My Documents\Matlab folder, or on OSX > Documents\Matlab
Open ReadEPW.m This is an updated version and does not require you to manually input the file path.
Click Run and locate the desired Energy+ Weather file
Open EPW2Hourly.m and click Run
Type in command:
psychro(dbt,ahm,0,0,’scatter’);
This will create a scatter plot of all the hourly values of dry bulb temperature and absolute humidity.
To separate all the data by months, use the command
psychro(dbt(aug),ahm(aug ),0,0,’scatter’);

psychro(dbt(apr),ahm(apr),0,0,’scatter’);
Sometimes having all the hourly data is not very useful because there’s simply too much information. Run the EPW2Daily.m file, this will reshape the 1×8760 matrix to 24×365, and we can easily find the daily maximum and minimum, and plot the diurnal changes.
Type in command:
psychro(dbtMax,dbtMin,ahmMax,ahmMin,’line’);
This will give you a line plot of all the diurnal changes. The line will start at the higher temperature and end at the lower temperature.
Once again, we can separate it into individual months with the following command
psychro(dbtMax(aug),dbtMin(aug),ahmMax(aug),ahmMin(aug),’line’);
psychro(dbtMax(apr),dbtMin(apr),ahmMax(apr),ahmMin(apr),’line’);

If you get an error like this one

??? Error using ==> fgetl at 44

Invalid file identifier. Use fopen to generate a valid file

identifier.

Error in ==> ReadEPW at 16

loc = fgetl(fid);

That means Matlab is not recognizing the folder of your EPW file. Go to File > Set Path and locate the folder.

To set a different color for each month and automatically save the charts, you can do the following.
%written and copyrighted by Ted Ngai www.tedngai.net %version 0.1 Mar 2011


xSize = 6; %This is the horizontal size of the figure

ySize = 4; %This is the vertical size of the figure

months = {1:31, 32:59, 60:90, 91:120, 121:151, 152:181, 182:212, 213:243, 244:273, 274:304, 305:334, 335:365};

monthstr = {'jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'};

figure;

c=0;

for x = 1:12
    %get current month

    month = cell2mat(months(x))';
    %get min/max of month

    maxmon=max(month);

    minmon=min(month);
    %set color value

    nColor=[1,0.8-c,0.1+c];

    c=c+0.07;
    %plot graph

    psychro(dbtMax(month),dbtMin(month),ahmMax(month),ahmMin(month),'line',nColor);
    xlabel(cell2mat(monthstr(x)));  %.. and label for the y axis

    %ylabel('temperature (\circC)');   %Input the labels for the x axis
    currentFig = gcf;

    currentAxes = gca;
    set(currentAxes,'FontSize',6);

    set(currentFig, 'PaperUnits', 'inches'); %change paperspace units here (also line 25)

    set(currentFig, 'PaperSize', [xSize ySize]);

    set(currentFig, 'PaperPositionMode', 'manual');

    set(currentFig, 'PaperPosition', [0 0 xSize ySize]);

    set(currentFig, 'Units', 'inches'); %change paperspace units here too

    set(currentFig, 'Position', [0, 0, xSize, ySize]);
    axis([minmon maxmon 0 120]);
    fn=['psychro_daily_',city,num2str(x),'.ai'];

    hgexport(currentFig,fn); %Change the filename here

end clear xSize ySize currentFig currentAxes fn temp maxmon minmon month ;

This entry was posted on Sunday, February 27th, 2011 at 3:08 pm and is filed under teaching and tagged with data visualization, psychrometrics, sustainability, weather data. You can follow any responses to this entry through the RSS 2.0 feed.