Architectural Engineering Design 1 Computing Part Term Project: Data Analysis and Visualization
Architectural Engineering Design 1
Computing Part
Term Project Brief
A Simple Building Performance Simulation Software
80% towards the “computing part” of ABEE1025
This is a term-long project (coursework) which you need to start from the first day of the semester. During the semester, we will learn the necessary knowledge and skills to facilitate the successful completion of this coursework. After the end of this semester, if you work hard enough, you will be able to submit a simple but complete software for building performance simulation.
Learning outcomes
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Test and practice the knowledge of using Python and building performance simulation engine to design and implement a complete software package
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Test and practice the knowledge of using computing skills to solve engineering problems related to Architectural Environment Engineering
Release and Submission
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Release:Sept20th
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Due: The coursework is separated into a few parts, different parts have different
due dates.
Format
Individual
SubmissionoverMoodlePart 1 – Data Analysis and Visualization (25%)
In this part of coursework, you will use a given CSV file and perform basic data analysis and visualization. You will also use data analysis packages of Python, such as Pandas, and Matplotlib.
Submission
Due:26Oct2023,16:00PM
Submissionformat:submitajupyternotebookfileonMoodleLearning outcomes
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Test and practice the knowledge of using Pandas and Matplotlib to process and visualize data in a CSV file.
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Test and practice the knowledge of simple data analysis on building performance
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data
Requirements:
In this coursework, you are given a full year operation data in a csv file of a single-room building, including:
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- outdoor air temperature (Environment:Site Outdoor Air Drybulb Temperature
[C](TimeStep))
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- outdoor relative humidity (Environment:Site Outdoor Air Relative Humidity
[%](TimeStep))
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- direct solar radiation (Environment:Site Direct Solar Radiation Rate per Area
[W/m2](TimeStep))
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- diffuse solar radiation (Environment:Site Diffuse Solar Radiation Rate per Area
[W/m2](TimeStep))
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- indoor air temperature (ZONE ONE:Zone Mean Air Temperature [C](TimeStep))
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- indoor relative humidity (ZONE ONE:Zone Air Relative Humidity [%](TimeStep))
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- AC heating power (ZONE 1 IDEAL LOADS:Zone Ideal Loads Supply Air Total Heating
Rate [W](TimeStep))
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- AC cooling power (ZONE 1 IDEAL LOADS:Zone Ideal Loads Supply Air Total Cooling
Rate [W](TimeStep))
We would like to perform analysis on the data to discover what are the major impacting factors on building AC energy consumption, i.e. AC heating power and AC cooling power. You need to use Python to answer the following questions:
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What is the heating and cooling consumption profile of the building? Generate the following 3 charts using Python:
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a) One bar chart of monthly total heating energy consumption (in kWh) and monthly total cooling energy consumption (kWh). Note: you should use different colors to represent heating energy and cooling energy respectively.
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b) One line chart of daily (weekends data should be ignored) total heating energy consumption (in kWh) and daily (weekends data should be ignored) total cooling energy consumption (kWh). Note: you should use different colors to represent heating energy and cooling energy respectively.
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c) One line chart of hourly average cooling energy rate (kW) of the day where the daily totally cooling energy consumption is the maximum, and hourly average heating energy rate (kW) of the day where the daily totally heating energy consumption is the maximum. Note: you should use different colors to represent heating energy and cooling energy respectively.
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What is the relationship between heating and cooling energy consumption versus weather conditions? Generate the following 10 charts using Python:
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a) One scatter plot of hourly average cooling energy rate (kW) versus hourly average outdoor dry-bulb temperature (C). Note: data points where the hourly average cooling energy rate is zero should be ignored.
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b) One scatter plot of hourly average cooling energy rate (kW) versus hourly
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average direct solar radiation (W/m2). Note: data points where the hourly
average cooling energy rate is zero should be ignored.
c) One scatter plot of hourly average cooling energy rate (kW) versus hourly
average diffuse solar radiation (W/m2). Note: data points where the hourly
average cooling energy rate is zero should be ignored.
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d) One scatter plot of hourly average cooling energy rate (kW) versus hourly
average total solar radiation (diffuse solar radiation + direct solar radiation, W/m2). Note: data points where the hourly average cooling energy rate is zero should be ignored.
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e) One scatter plot of hourly average cooling energy rate (kW) versus hourly average dewpoint temperature (C). Note: data points where the hourly average cooling energy rate is zero should be ignored. Hint: dewpoint temperature can be calculated using dry-bulb temperature and relative humidity, try to google it.
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f) One scatter plot of hourly average heating energy rate (kW) versus hourly average outdoor dry-bulb temperature (C). Note: data points where the hourly average heating energy rate is zero should be ignored.
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g) One scatter plot of hourly average heating energy rate (kW) versus hourly average direct solar radiation (W/m2). Note: data points where the hourly average heating energy rate is zero should be ignored.
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h) One scatter plot of hourly average heating energy rate (kW) versus hourly average diffuse solar radiation (W/m2). Note: data points where the hourly average heating energy rate is zero should be ignored.
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i) One scatter plot of hourly average heating energy rate (kW) versus hourly average total solar radiation (diffuse solar radiation + direct solar radiation, W/m2). Note: data points where the hourly average heating energy rate is zero should be ignored.
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j) One scatter plot of hourly average heating energy rate (kW) versus hourly average dewpoint temperature (C). Note: data points where the hourly average heating energy rate is zero should be ignored. Hint: dewpoint temperature can be calculated using dry-bulb temperature and relative humidity, try to google it.
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What you can find from the above 13 charts? Write a brief analysis report (less than 300 words) and the report should analyze on the building energy consumption profile and which weather factor(s) have obvious impact on heating and cooling energy consumption.
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(BONUS) Quantify the correlation between each weather factor and heating/cooling energy consumption. You may consider to use Pearson correlation coefficient or other similar ones.
Marking Rubrics:
1. Charts: each chart is worth 6 marks
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a) The chart has correct data, including unit and time resolution – 4 marks
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b) The chart has clear legends, labels on x and y axis, x and y axis titles, correct
plotting color, suitable figure size, suitable font size. Each violation will be 0.5
mark penalty. 2. Analysis: 9 marks
a) Clear and correct analysis on the impacts of each weather factor on heating and cooling energy consumption, with strong reference to the charts that have been plotted; correct English usage without grammar errors.
3. Bonus: 10 marks
a) Correct use and calculation of correlation coefficient
