长沙长沙网站建设公司,门户网站建设报价,义乌企业网站设计,品牌大全#x1f4a5;#x1f4a5;#x1f49e;#x1f49e;欢迎来到本博客❤️❤️#x1f4a5;#x1f4a5; #x1f3c6;博主优势#xff1a;#x1f31e;#x1f31e;#x1f31e;博客内容尽量做到思维缜密#xff0c;逻辑清晰#xff0c;为了方便读者。 ⛳️座右铭欢迎来到本博客❤️❤️ 博主优势博客内容尽量做到思维缜密逻辑清晰为了方便读者。 ⛳️座右铭行百里者半于九十。 本文目录如下 目录 1 概述 2 运行结果 3 参考文献 4 Matlab代码及数据 1 概述
在本文中MATLAB 用于通过与使用 XBee 系列 2 模块构建的温度传感器无线网络进行交互连续监控整个公寓的温度。每个XBee边缘节点从多个温度传感器读取模拟电压与温度成线性比例。读数通过协调器XBee模块传输回MATLAB。本文说明了如何操作、获取和分析来自连接到多个 XBee 边缘节点的多个传感器网络的数据。数据采集时间从数小时到数天不等以帮助设计和构建智能恒温器系统。
2 运行结果 部分代码
%% Overview of Data % As I mentioned in a previous post, I collected the temperature every two % minutes over the course of 9 days. I placed 14 sensors in my apartment: 9 % located inside, 2 located outside, and 3 located in radiators. The data % is stored in the file ../twoweekstemplog.txt |twoweekstemplog.txt|.
[tempF,ts,location,lineSpecs] XBeeReadLog(twoweekstemplog.txt,60); tempF calibrateTemperatures(tempF); plotTemps(ts,tempF,location,lineSpecs) legend(off) xlabel(Date) title(All Data Overview)
%% % _Figure 1: All temperature data from a 9 day period._ % % That graph is a bit too cluttered to be very meaningful. Let me remove % the radiator data and the legend and see if that helps.
notradiator [1 2 3 5 6 7 8 9 10 12 13]; plotTemps(ts,tempF(:,notradiator),location(notradiator),lineSpecs(notradiator,:)) legend(off) xlabel(Date) title(All Inside and Outside Temperature Data)
%% % _Figure 2: Just inside and outside temperature data, with radiator data removed._ % % Now I can see some places where one of the outdoor temperature sensors % (blue line) gave erroneous data, so lets remove those data points. This % data was collected in March in Massachusetts, so I can safely assume the % outdoor temperature never reached 80 F. I replaced any values above 80 F % with |NaN| (not-a-number) so they are ignored in further analysis.
outside [3 10]; outsideTemps tempF(:,outside); toohot outsideTemps80; outsideTemps(toohot) NaN; tempF(:,outside) outsideTemps;
plotTemps(ts,tempF(:,notradiator),location(notradiator),lineSpecs(notradiator,:)) legend(off) xlabel(Date) title(Cleaned-up Inside and Outside Temperature Data)
%% % _Figure 3: Inside and outside temperature data with erroneous data removed._ % % Ill also remove all but one inside sensor per room, and give the % remaining sensors shorter names, to keep the graph from getting too % cluttered.
show [1 5 9 12 10 3]; location(show) ... {Bedroom,Kitchen,Living Room,Office,Front Porch,Side Yard};
plotTemps(ts,tempF(:,show),location(show),lineSpecs(show,:)) ylim([0 90]) legend(Location,SouthEast) xlabel(Date) title(Summary of Temperature Data)
%% % _Figure 4: Summary of temperature data with only one inside temperature % sensor per room with outside temperatures._ % % That looks much better. This data was collected over the course of 9 % days, and the first thing that stands out to me is the periodic outdoor % temperature, which peaks every day at around noon. I also notice a sharp % spike in the side yard (green) temperature on most days. My front porch % (blue) is located on the north side of my apartment, and does not get % much sun. My side yard is on the east side of my apartment, and that % spike probably corresponds to when the sun hits the sensor from between % my apartment and the building next door.
%% When do my radiators start to heat up? % The radiator temperature can be used to measure how long it takes for my % boiler and radiators to warm up after the heat has been turned on. Lets % take a look at 1 day of data from the living room radiator:
% Grab the Living Room Radiator Temperature (index 11) from the |tempF| matrix. radiatorTemp tempF(:,11);
% Fill in any missing values: validts ts(~isnan(radiatorTemp)); validtemp radiatorTemp(~isnan(radiatorTemp)); nants ts(isnan(radiatorTemp)); radiatorTemp(isnan(radiatorTemp)) interp1(validts,validtemp,nants);
% Plot the data oneday [ts(1) ts(1)1]; figure plot(ts,radiatorTemp,k.-) xlim(oneday) xlabel(Time) ylabel(Radiator Temperature (\circF)) title(Living Room Radiator Temperature) datetick(keeplimits) snapnow
%% % _Figure 5: One day of temperature data from the living room radiator._ % % As expected, I see a sharp rise in the radiator temperature, followed by % a short leveling off (when the radiator temperature maxes out the % temperature sensor), and finally a gradual cooling of the radiator. Let % me superimpose the rate of change in temperature onto the plot.
tempChange diff([NaN; radiatorTemp]);
hold on plot(ts,tempChange,b.-) legend({Temperature, Temperature Change},Location,Best)
%% % _Figure 6: One day of data from the living room radiator with temperature change._ % % It looks like I can detect those peaks by looking for large jumps in the % temperature. After some trial and error, I settled on three criteria to % identify when the heat comes on: % % # Change in temperature greater than four times the previous change in temperature. % # Change in temperature of more than 1 degree F. % # Keep the first in a sequence of matching points (remove doubles)
fourtimes [tempChange(2:end)abs(4*tempChange(1:end-1)); false]; greaterthanone [tempChange(2:end)1; false]; heaton fourtimes greaterthanone; doubles [false; heaton(2:end) heaton(1:end-1)]; heaton(doubles) false;
%% % Lets see how well I detected those peaks by superimposing red dots over % the times I detected.
figure plot(ts,radiatorTemp,k.-) hold on plot(ts(heaton),radiatorTemp(heaton),r.,MarkerSize,20) xlim(oneday); datetick(keeplimits) xlabel(Time) ylabel(Radiator Temperature (\circF)) title(Heat On Event Detection) legend({Temperature, Heat On Event},Location,Best)
%% % _Figure 7: Radiator temperature with heating events marked with red dots._ % % Looks pretty good, which means now I have a list of all the times that % the heat came on in my apartment. heatontimes ts(heaton);
%% How long does it take for my heat to turn on? % I currently have a programmable 5/2 thermostat, which means I can set % one program for weekdays (Monday through Friday) and one program for both % Saturday and Sunday. I know my thermostat is set to go down to 62 at % night, and back up to 68 at 6:15am Monday through Friday and 10:00am on % Saturday and Sunday. I used that knowledge to determine how long after my % thermostat activates that my radiators warm up. % % I started by creating a vector of all the days in the test period. I % removed Monday because I manually turned on the thermostat early that day. mornings floor(min(ts)):floor(max(ts)); mornings(2) []; % Remove Monday
%% % Then I added either 6:15am or 10:00am to each day depending on whether it % was a weekday or a weekend. isweekend weekday(mornings) 1 | weekday(mornings) 7; mornings(isweekend) mornings(isweekend)10/24; % 10:00 AM mornings(~isweekend) mornings(~isweekend)6.25/24; % 6:15 AM
%% % Next I looked for the first time the heat came on after the programmed % time each morning. heatontimes_mat repmat(heatontimes,1,length(mornings)); mornings_mat repmat(mornings,length(heatontimes),1); timelag heatontimes_mat - mornings_mat; timelag(timelag0) NaN; plot(ts,radiatorTemp,k.-) hold on plot(heatontimes,heatontemp,r.,MarkerSize,20) plot(heatontimes(heatind),heatontemp(heatind),bo,MarkerSize,10) plot([mornings;mornings],repmat(ylim,1,length(mornings)),b-); xlim(onemorning); datetick(keeplimits) xlabel(Time) ylabel(Radiator Temperature (\circF)) title(Detection of Scheduled Heat On Events) legend({Temperature, Heat On Event, Scheduled Heat On Event,... Scheduled Event},Location,Best)
%% % _Figure 8: Six hours of radiator data, with a blue line indicating when % the thermostat turned on in the morning, and blue circle indicating the % corresponding heat on event of the radiator._ % % Lets look at a histogram of those delays: figure hist(delay,min(delay):max(delay)) xlabel(Minutes) ylabel(Frequency) title(How long before the radiator starts to warm up?)
%% % _Figure 9: Histogram showing delay between thermostat activation and the % radiators starting to warm up._ % % It looks like the delay between the thermostat coming on in the morning % and the radiators starting to warming up can range from 7 minutes to as % high as 24 minutes, but on average this delay is around 12-13 minutes. heatondelay 12;
%% How long does it take for the radiators to warm up? % Once the radiators start to warm up, it takes a few minutes for them to % reach full temperature. Lets look at how long this takes. Ill look for % times when the radiator temperature first maxes out the temperature % sensor after having been below the maximum for at least 10 minutes (5 % samples).
maxtemp max(radiatorTemp); radiatorhot radiatorTemp(6:end)maxtemp ... radiatorTemp(1:end-5)maxtemp ... radiatorTemp(2:end-4)maxtemp ... radiatorTemp(3:end-3)maxtemp ... radiatorTemp(4:end-2)maxtemp ... radiatorTemp(5:end-1)maxtemp; radiatorhot [false(5,1); radiatorhot]; radiatorhottimes ts(radiatorhot); % % Now Ill match the |radiatorhottimes| to the |heatontimes| using the same % technique I used above. radiatorhottimes_mat repmat(radiatorhottimes,length(heatontimes),1); heatontimes_mat repmat(heatontimes,1,length(radiatorhottimes)); timelag radiatorhottimes_mat - heatontimes_mat; timelag(timelag0) NaN; [delay, foundmatch] min(timelag); delay round(delay*24*60);
%% % Lets look at a histogram of those delays: figure hist(delay,min(delay):2:max(delay)) xlabel(Minutes); ylabel(Frequency) title(How long does the radiator take to warm up?)
%% % _Figure 11: Histogram showing time required for the radiators to warm up._ % % It looks like the radiators take between 4 and 8 minutes from when they % start to warm up until they are at full temperature. radiatorheatdelay 6;
%% % Later on in my analysis, I will only want to use times that the heat came % % Although it isnt perfect, it looks close to a linear relationship. Since % I am interested in the time it takes to reach the desired temperature % (what could be considered the specific heat capacity of the room), let % me replot the data with time on the y-axis and temperature on the x-axis % (swapping the axes from the previous figure). Ill also plot the data as % individual points instead of lines, because that is how the data is going % to be fed into |polyfit| later.
% Remove temperatures occuring before the minimum temperature. segmentTempsShifted(segmentTimesShifted0) NaN;
figure h1 plot(segmentTempsShifted,segmentTimesShifted,k.); xlabel(Temperature Increase (\circF)) ylabel(Minutes since minimum temperature) title(Time to Heat Living Room) snapnow
%% % _Figure 17: The time it takes to heat the living room (axes flipped from % Figure 16)._ % % Now let me fit a line to the data so I can get an equation for the time % it takes to heat the living room.
%% % First I collect all the time and temperature data into a single column % vector and remove |NaN| values.
allTimes segmentTimesShifted(:); allTemps segmentTempsShifted(:); allTimes(isnan(allTemps)) []; allTemps(isnan(allTemps)) [];
%% % Then I can fit a line to the data. linfit polyfit(allTemps,allTimes,1);
%% % Lets see how well we fit the data.
hold on h2 plot(xlim,polyval(linfit,xlim),r-); linfitstr sprintf(Linear Fit (y %.1f*x %.1f),linfit(1),linfit(2)); legend([ h1(1), h2(1) ],{Data,linfitstr},Location,NorthWest)
%% % _Figure 18: The time it takes to heat the living room along with a linear fit to the data._ % % Not a bad fit. Looking closer at the coefficients from the linear fit, it % looks like it takes about 3 minutes after the radiators start to heat up % for the room to start to warm up. After that, it takes about 5 minutes % for each degree of temperature increase.
%% What room takes the longest to warm up? % I can apply the techniques above to each room to find out how long each % room takes to warm up. I took the code above and put it into a separate % function called ../temperatureAnalysis.m |temperatureAnalysis|, and % applied that to each inside temperature sensor.
inside [1 5 9 12];
figure xl [0 14]; for s 1:size(inside,2) linfits(s,1:2) temperatureAnalysis(tempF(:,inside(s)), heaton, heatoff); y polyval(linfits(s,1:2),xl) heatondelay; plot(xl, y, lineSpecs{inside(s),1}, Color,lineSpecs{inside(s),2},... DisplayName,location{inside(s)}) hold on end legend(Location,NorthWest) xlabel(Desired temperature increase (\circF)) ylabel(Estimated minutes to heat) title(Estimated Time to Heat Each Room)
%% % _Figure 19: The estimated time it takes to heat each room in my apartment._
3 参考文献 部分理论来源于网络如有侵权请联系删除。 [1]王晓银.基于XBee的瓦斯无线传感器网络节点的设计[J].自动化技术与应用,2018,37(08):46-49.
[2]王晓银.基于XBee的瓦斯无线传感器网络节点的设计[J].自动化技术与应用,2018,37(08):46-49.
4 Matlab代码及数据
