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Investigating Intrinsic Energy Consumption Seasonality Adedamola Adepetu, Elnaz Rezaei

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outline Introduction Problem Definition Data Sets Our Approach Challenges 2

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

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daily season 4 Time of day

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weekly season 5

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annual season 6 Summer Winter

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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

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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

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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

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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

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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

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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

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observing seasonality Autocorrelation –Includes Partial Autocorrelation 13

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

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

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

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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

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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

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

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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 http://www.theimo.com/imoweb/marketdata/marketdata.asp. http://www.theimo.com/imoweb/marketdata/marketdata.asp (OEB), Ontario Energy Board. Electricityprices. Accessed on 18 October 2012, at http://www.ontarioenergyboard.ca/OEB/Consumers/Electricity/Electricity+Prices. http://www.ontarioenergyboard.ca/OEB/Consumers/Electricity/Electricity+Prices 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 https://www.statsoft.com/textbook/time-series-analysis/. https://www.statsoft.com/textbook/time-series-analysis/ Fox, J. (2002). Bootstrapping Regression Models. Annals of Statistics 9, 1-14. Available at: http://www.jstor.org/stable/10.2307/2240411. http://www.jstor.org/stable/10.2307/2240411 http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc4463.htm 20

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