This document analyzes electricity net generation data from the United States Energy Information Administration from 1973 to 2015 using time series analysis and ARIMA modeling. It finds that electricity net generation has an increasing trend over time with seasonal peaks in the summer months. An ARIMA(1,1,1)(0,1,1)[12] model is shown to fit the data well and forecast a 1.63% increase in generation over the next 17 months with a validation forecast for August 2015 being within 0.41% of actual reported values. However, the analysis notes that further regression analysis could provide more insights into impacts of different generation sources over time.

1. Electricity Net Generation in U.S
Time series analysis and forecasting
Shen (Carol) Yan, Shih-Wen (Elsa) Huang

2. 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

3. 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

4. 39%
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
35%
1%0%
32%
0%
19%
6%
7% 0%0%
2015 Electricity generation sources (till August)
coal
petroleum liquids
petroleum coke
natural gas
other gas
nuclear
hydroelectric
conventional
renewable source
pump
other
Electricity sources

5. Objectives
1. The model behavior of this dataset
2. Create the fitting model to forecast the following
electricity generation in next 17 month till December
2016.

6. Time plot of electricity generation
 Trend: Increasing trend
 Seasonality
 Spikes - Something happened in 2009: about price
2009
Electricity net generation
decreased

7. Before building the model
 Detrend
 Deseaonalization

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

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

10. 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)

11. 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.

12. Forecast
 Point forecast for following 17 months

13. 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 Forecast
August 2015 392298 393923
* EIA: Energy Information Administra
Fit well!

14. 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.

15. Reference
 http://www.eia.gov/todayinenergy/detail.cfm?id=8450
 http://www.eia.gov/electricity/monthly/epm_table_grapher.cfm?t=e
pmt_1_01