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Justin_CER

CER Smart Meter Project by Irish Social Science Data Archive. During my study at Duke University, I worked as a RA under the guidance of Danton Noriega: https://github.com/ultinomics and Prof. Matthew Harding: http://people.duke.edu/~mch55/

#Notes on Documentation and Paper Reading

##Background

####CER Customer Behavior Trial, by ISSDA

  1. Electricity Customer Behaviour Trial: During 2009 and 2010, with over 5,000 Irish homes and businesses Elec Time

  2. Gas Customer Behaviour Trial: During 2010 and 2011 with nearly 2,000 Irish homes. Gas Time

##Folders of 'CER Electricity March 2012'

Check out the 'Manifest' word file, important

#####(1) Smart meter read data:

  • 6 zip files, each around 1 million records
  • Units are 30 minute interval (in kW), then multiply 0.5 hour. Thus all kwh records in the should be divieded by 2

#####(2) Pre and post trial residental surveys:

  • Pre: about 100 questions, 4200 answer participants, ID_survey=IDmeter
  • Post: about 120 questions, 3400 answer participants

#####(3) Pre and post trial SME surveys:

  • Pre: about 80 questions, 330 answer participants
  • Post: about 120 questions, 288 answer participants

#####(4) Allocation Excel file (.csv and .xlsx)

  • a graph showing the details of tariff price added to .xlsx
  • In the surveys we can also know which ID is control/treatment group, and what tariff+stimulus taken, but this file is more accurate

##Folder of 'CER Gas October 2012'

Check out the 'Manifest' word file, important

#####(1) Smart meter read data:

  • 78 Excel files, each 0.5 million records

#####(2) Pre and post trial residental surveys:

  • Pre: about 130 questions, 1360 answer participants
  • Post: about 200 questions, 1200 answer participants
  • A quick view of survey questions: in the order of the file
  • Segmentation: gender, age (7 groups), employment,social status(A,B,C1,C2,D,E,F), family members+children, rent/own
  • Attitude: if willing to change, if thinking this project important, etc.
  • Behavior:
    • Q 470 how to heat home,
    • Q 54192 and 47031 if they know the rates are changing
    • Q 54131 household change behavior or not
    • Q 5414 if bill, overall usage % changes, peak hour
    • Q 5419 tarrif
    • Q 5420 5-7pm factors
    • Q 6022 monitor
    • Q 6520 Statement
    • Q 6601 OLR

#####(3) Allocation Excel file

##Folder of 'Documentation' ###1. cer09163: Talked in detail of Domestic ToU tarrif (A,B,C,D), weekend tarrif (W), and genertal perpose tarrif(A,B) tarrif small ###2. cer11080: For Electric trial

  • This file is the conclusion of cer11080(a), cer110080(b), cer11080(c). Cer11080(a) also has (a)(i) and (a)(ii). These files can all be Google found.
  • The structure of cer11080:
    • 2.0. Underlining technology: not something we care about
    • 3.0. CBT key findings: Eg. reduce overall electricity usage by 2.5% and peak usage by 8.8%; reducing peak usage with a peak shift of 11.3%; 82% of participants made some change to the way they use electricity due to the Trial with 74% stating major changes were made by their households
      • Family members, No. of children are important
      • Different tarrifs are not so effective, difference in stimulus is
      • In the Excel of post survey we have a list of: OLR (58% recalled using), IHD, Statement 1, Statement 2(per month or bi-month; different from bill), Tarrif, stimulus
    • 4.0. Cost-benefit analysis: net present value (NPV) of the project in 2011
      NPV
    • 5.0. Future steps

###3. cer11080(a): ####11080(a)(i): very important, every detail of how the electric trial is designed, and how data analyzed. Begin reading from 6.0. :Approach to Data Analysis Some take aways-

  • Detect a minimum effect of a 2% change in usage over the whole sample of participants and between Tariff A and Tariff C. A change of 3% would be detected at the level of Tariffs B and D and the DSM stimuli and a reduction of 4% would be detected at a Tariffs A and C and at a cellular level. A 90% confidence level is applied to all tests conducted.

Stimulus

  • Distribution of the data frequency mean

  • The way they show % change: Rt and Rc: (Page 60). They looked at Rt-Rc as the % decrease

ratio

  • P65-P93, Residental key results. For example, based on the above defined ratio, we have the following % decrease; also the segmentation results. After P94 is SME key results.

% decrease segementation

####11080(a)(ii): More data-analysis method(P21~28)+ visualization, important

  • A2.2 talks about the hypothesis test, 90% confidence level

  • Missing data: (zero usage being recorded). Specifically, data is missing for a single day of the study for 114 meters and for two days for 42 meters. 4225=4069+114+42, and there should be around 700 meters missing all the records.

  • P26 defined Night, Day, Peak time. P27 verification of sum. P30 gives Tp test between ratios
    sum t p

  • The rest pages are detailed results of segementations, focus group, Pre and Post-survey analysis

###3. cer11180, cer11180a, cer11180b: For Gas trial

Notes of Codes and Results: On GitHub

justinishere (GitHub)

##Folder of results

1. summary table: mean and SD of segmentation

  • Revising from sheet 1 to sheet 3. Sheet 1 all based on post survey anser
  • sheet 2 merged answers of pre-survey with post-survey
  • Sheet 3 realized missing data was not eliminated before

2. daily mean

  • mean= sum usage of that day/Number of meters；16 graphs of mean of daily electricity usage
  • No. of households: A around 290 each, B 110, C 299, D 105
  • distribution
    • A1-4 treatment > control
    • B1 treatment< control， B 2-3 treatment > control, B4 treatment >> control
    • C1-4 treatment > control
    • D1 treatment = control, D2 and 3 treatment > control, D3 treatment < control

3. sum all

  • daily and monthly sum of electricity usage- all treatment groups together

4. sum_month

  • The Excel is monthly sum of electricity usage-16 different groups
  • This file can be used to calculate the ratio Rt and Rc
  • The folder 'sum month' is the detailed data

5. sum_month_id

  • This is monthly sum of each smart meter ID of 16 different groups
  • T-test by month, with graphs. We have A1, B4, B1 vs. Control now.
  • The folder 'sum month id' is the detailed data

6. summary_day_id

  • Daily t stat calculated from mean and SD, with graphs
  • The folder is the detailed data

7. T-est_day_id

  • Aggregated T-test graphs of all 16 treatment groups

8. B1 and D3: chosen based on daily mean and t-test

##Folder of code

1. reading_win1

  • Time series: I manually checked the time_series correction file with my method. Both correct. (Benchmark period 19501 to 36548; test period 36600 to 73048)
  • For date of segmentation I used the manual time series method

2. reading_dan: Danton's sample code

3. daily test: a 'laborous' version of getting daily means

4. mean+sum check

  • codes where most of the Excel results derived

##Folder of markdown

  • .md files and graphs used

##Folder of 'Paper'-other publications on CER

  • 'Knime' used clustering method; on P8 we have the reference for actual unit (kw * 30 minutes), not Kwh

see also the 'time_series correction' and 'allocation'

#Acknowledgements

Danton Noriega has helped me a lot with R programming. He is a mater. I would also like to appreciate Matthew Harding for giving me the chance of working on this project.