Difference between revisions of "User:Maria.Dela-Sancha"
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==Research Topic== | ==Research Topic== | ||
| + | Computing daily, monthly, and yearly gas usage in residential and commercial buildings is crucial for gas companies. When analyzing gas consumption, data analyst must look | ||
| + | at all the factors that contribute to this total usage in order to come up with ways to save energy. Examples of these factors are; building structures, the economy, type of heating and cooling system being used, and one of the main factors that needs a lot of research is the weather. The weather is probably one of the main factors affecting consumption, but how can we understand the relationship between weather and consumption when the weather is never normal. When trying to summarize effectiveness of a new energy-saving heating system, one must consider that the weather last year was not the same at it was this year. In order to understand true gains and losses, weather normalization techniques are employed to gas consumption algorithms. To this day there is only a couple algorithms in use and not much research done on them. All of these algorithms also show a general idea of what we expect to see, but we still don't understand how to test for an exact measurement of effectiveness. We also don't understand how to analyze which algorithm is better when they all show similar results and when we still don't understand what complete normal weather looks like. | ||
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==Weekly Accomplishments== | ==Weekly Accomplishments== | ||
===Week 1=== | ===Week 1=== | ||
Revision as of 15:23, 13 July 2015
Contents
Weather Normalization
Personal Info
My name is Maria Dela Sancha from Zion, Illinois. I am NROTC student at Marquette University majoring in Mathematics with a minor in Computer Science, Class of 2016. I will be researching Weather Normalization for the Gas-Day Lab, with mentors, Dr. Corliss, Dr. Povinelli, and Dr. Brown.
Research Topic
Computing daily, monthly, and yearly gas usage in residential and commercial buildings is crucial for gas companies. When analyzing gas consumption, data analyst must look at all the factors that contribute to this total usage in order to come up with ways to save energy. Examples of these factors are; building structures, the economy, type of heating and cooling system being used, and one of the main factors that needs a lot of research is the weather. The weather is probably one of the main factors affecting consumption, but how can we understand the relationship between weather and consumption when the weather is never normal. When trying to summarize effectiveness of a new energy-saving heating system, one must consider that the weather last year was not the same at it was this year. In order to understand true gains and losses, weather normalization techniques are employed to gas consumption algorithms. To this day there is only a couple algorithms in use and not much research done on them. All of these algorithms also show a general idea of what we expect to see, but we still don't understand how to test for an exact measurement of effectiveness. We also don't understand how to analyze which algorithm is better when they all show similar results and when we still don't understand what complete normal weather looks like.
