Energized Learning: Bringing It Home
Team Number/Name: _______________ Date: __________
Student Name(s): _________________________________
Weather City: _______________ Zip Code: __________
Welcome to the Energized Learning Site. You will begin by establishing an account in the Home Energy Saver
(http://hes.lbl.gov) and printing your first
records for analysis and comparison of energy use and efficiency as you change in your home's description, services, and energy
efficiency levels. All you need to get started is your zip code.
Go to The Home Energy Saver web site
and enter your Zip Code. You will obtain you first estimated energy use information for a typical house in that zip
- Please read the information on the home page for the Energized Learning web site:
On the resulting page, answer as many of the questions as you can and select "Save Answers". Don't worry about mistakes or approximations; you will be able to modify and improve your input later. On the resulting page click on "Calculate." Print the resulting page, which now contains your "session number". Keep this page for future reference. Click on the "See greenhouse gas emissions and energy consumption" button and print the resulting page. The work up to this point should take no more than 10-15 minutes.
The teacher will discuss the meaning of the information on these pages.
Here are some questions to think about and discuss.
You will now have about 25 minutes to make changes to your house so that it more nearly reflects the home you have. Print
your results and "carbon emission or energy consumption" results.
- What are the sources of emissions (combustion of fossil fuels)
- Can you name some of the sinks for CO2? (Photosynthesis, oceans, others)
- Why is CO2 listed as a pollutant in the Home Energy Saver (Greenhouse gas that has the potential to cause climate change)
Now its time to select one or more upgrades to make your house more energy efficient and recalculate the whole-house
energy costs, requirements and pollution. Print your results. You now have three sets of data to compare.
What is different before and after the upgrade(s)? What impact can you expect on your quality of life and energy use?
Would you consider energy efficiency an investment in services or an added expense in your household budget?
You will use your data and your classmates' data to explore the effect of variations and trends in energy requirements.
Consider what factors may be responsible for the trends that emerge from the data.
Note the range of concepts and information required to predict energy use.
As a class, compare your views about the initial energy efficiency concepts with your views at the beginning of the class.
Close the Browser Window and save your results for another session.
Send us your feedback and suggestions! (firstname.lastname@example.org)
Additional Exercises & Experiments
- Analyze the "outliers" in the scatter diagrams. What are possible physical reasons for the outliers? Could errors in
either the computer program or the user inputs be involved? If so, what kinds of errors? How do the statistical metrics
(standard deviation and % standard deviation) change if the outliers are removed?
- Run a series of calculations progressively increasing your ceiling insulation and draw a graph of the results. What
does the graph suggest?
- Find the equation of a line (least-squares fit in the form ax+b) through the correlation diagrams. This is a simplified
"model" for predicting energy use in homes. What is the y-intercept, and what does it represent? How accurate would this
- Create your "dream home", which may be larger and/or have more gadgets and thus use more energy. Find ways to improve
the efficiency (e.g. more insulation) so that energy use is no higher than the home you currently live in.
- Go on the web and find 10 images of the carbon cycle (Hint: do a
Google image search) Compare and contrast
the images and discuss their strengths and weaknesses both in terms of completeness and method of graphically telling the story.
- Look at the chart below and discuss the possible reasons for the large variability in predictive power of energy audit tools.