Download - Electricity Net Generation
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