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Investigating Intrinsic Energy Consumption Seasonality Adedamola Adepetu, Elnaz Rezaei.

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Presentation on theme: "Investigating Intrinsic Energy Consumption Seasonality Adedamola Adepetu, Elnaz Rezaei."— Presentation transcript:

1 Investigating Intrinsic Energy Consumption Seasonality Adedamola Adepetu, Elnaz Rezaei

2 outline Introduction Problem Definition Data Sets Our Approach Challenges 2

3 introduction Seasonality is a repetitive behavior observed in time series Intrinsic inherent repetitions in load Days, Weeks, Months, Calendar seasons 3

4 daily season 4 Time of day

5 weekly season 5

6 annual season 6 Summer Winter

7 problem definition Does the intrinsic load seasonality correspond to predefined seasons? Why does this seasonality exist (if any)? How is this affected by exogenous factors: temperature, prices, hours of day etc.? 7

8 expected results Establish a procedure for determining actual load seasonality, resulting in: Improved electricity pricing structure More informed load prediction process Storage sizing for peak load reduction 8

9 datasets: ieso Description: Aggregate load for the whole province of Ontario Time Period: 2002 till date Resolution: 1 hour Weather data available from Environment Canada Electricity prices also available This is at the macro level 9

10 datasets: essex Description: Load profiles for 6800 homes in Windsor, Ontario Time Period: 1.5 years Resolution: 1 hour Weather data available from Environment Canada This is the micro level Also contains outages 10

11 datasets: irish Description: Load profiles for 4560 homes, somewhere in Ireland Time Period: 1.5 years Average weather data for Ireland? Resolution: 30 minutes Electricity & gas 11

12 our approach Observe (without preconception) seasonality in all datasets Determine impact of exogenous features Match macro & micro levels, i.e., Ontario & Essex patterns Validation 12

13 observing seasonality Autocorrelation –Includes Partial Autocorrelation 13

14 observing seasonality Fourier series analysis –Spectral analysis 14 Time (months)

15 observing seasonality Additive model y = trend + seasonal_component + error Multiplicative model y = trend *seasonal_component + error Aggregation of home load? Yes & No. 15

16 impact of exogenous features Temperature Hours of day & night Prices (any feedback?) Weekends & Holidays (not exogenous per se but still important) 16

17 regression Seasonal AutoRegressive Moving Average (SARMA) Seasonal ARIMA (verify nonstationarity of data) ARMAX? …X for exogenous Use regressive model from macro level as a feature at micro level 17

18 validation Bootstrapping …statistical inference based on building a sampling distribution for a statistic by resampling from the data at hand. – Fox, 2002 Used to establish confidence intervals 18

19 challenges What if we find nothing? Longer time periods required for micro- level datasets 19

20 references Bisgaard, Soren and Kulahci, Murat. John Wiley &Sons, Inc., 2011. ISBN 9781118056943. doi: 10.1002/9781118056943.ch9. Espinoza, M., Joye, C., Belmans, R., and DeMoor,B. Short-term load forecasting, prole identication, and customer segmentation: A methodologybased on periodic time series. Power Systems, IEEETransactions on, 20(3):1622 { 1630, aug. 2005. ISSN0885-8950. doi: 10.1109/TPWRS.2005.852123. (IESO), Independent Electricity System Operator.Market Data. Accessed on 18 October 2012,at (OEB), Ontario Energy Board. Electricityprices. Accessed on 18 October 2012, at Singh, R. P., Gao, P.X., and Lizotte, D. J. On Hourly Home Peak Load Prediction. In IEEE SmartGridComm 2012, 2012. StatSoft. How To Identify Patterns in Time Series Data: Time Series Analysis. Accessed on 18 October 2012, at Fox, J. (2002). Bootstrapping Regression Models. Annals of Statistics 9, 1-14. Available at: 20

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