Electricity Net Generation in U.STime series analysis and forecasting

Shen (Carol) Yan, Shih-Wen (Elsa) Huang

Motivation

We are curious whether time series confirm to our original assumption: winter has the highest net electricity generation.

Dataset from EIA has 511 observations and 2 variables: month and electricity net generation total

* EIA: Energy Information Administration

Background With the economic growth and industries development in

the U.S, the demand of electricity is increasing year by year. This phenomenon leads to higher electricity generation and also reflects on the dataset from January 1973 to July 2015:

Increasing trend- Total of electricity net generation increase per year.

Seasonal behavior

40%

1%0%27%

0%

19%

7%7% 0%0%

2014 Electricity generation sources

coal

petroleum liquids

petroleum coke

natural gas

other gas

nuclear

hydroelectric conventional

renewable source

pump

other

34%

0%0%

32%

0%

19%

6%7% 0%0%

2015 Electricity generation sources (till August)

coalpetroleum liquidspetroleum cokenatural gasother gasnuclearhydroelectric con-ventionalrenewable sourcepumpother

Electricity sources

Objectives1. The model behavior of this dataset2. Create the fitting model to forecast the

following electricity generation in next 17 month till December 2016.

Time plot of electricity generation Trend: Increasing trend Seasonality Spikes - Something happened in 2009: about price

2009Electricity net generation decreased

Before building the modelDetrendDeseaonalization

Detrend Detrend: Flat ACF & PACF: Simultaneously show seasonality in the time period of 12 month

Deseasonalization ACF & PACF: Dickey-Fuller test: p-value(0.01) <0.05, null hypothesis of non-stationary is rejected.

Build the model-SARIMA Model: ARIMA(1,1,1)(0, 1, 1)[12]

Test of coefficients: All parameters are significant.

Expression: (1-0.45B)(1-B)(1-B12)Xt=(1-0.90B)(1-0.73B12)

Diagnosis ACF plot of residuals: generally stationary L-jung Box tests: p-value>0.05, cannot reject White

Noise(residuals) Normal quantile plot:

Brief conclusion: The model SARIMA(1,1,1)(0,1,1)[12] is statistically acceptable and can be processed to explain and make a prediction.

Forecast Point forecast for following 17 months

Validation of model MAPE from Back-test: 1.63% Compare with the latest data announced by EIA and calculate new MAPE: 0.41%

  Released from EIA Our ForecastAugust 2015

392298 393923

* EIA: Energy Information Administration

Fit well!

Conclusion This is a non-stationary model with an increasing trend. Model has seasonal behavior: peak period is during summer. The forecasts for the following 17 months are consistent with

previous patterns. Our model is reliable: The specific forecast of August is with minor

error to the number announced by Energy Information Administration official website.

Limitation: Further research is needed on time series regression to identify impact of each source such as, petroleum, coal, nuclear and natural gas, etc., on electricity net generation in the U.S.

Reference http://www.eia.gov/todayinenergy/detail.cfm?id=8450

http://www.eia.gov/electricity/monthly/epm_table_grapher.cfm?t=epmt_1_01