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Evaluation of Data Quality and Imputation Methods for EIA’s Liquefied Natural Gas Storage Report

Abstract Number:

2987 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Makayla Cowles (1), Preston McDowney (2), Pushpal Mukhopadhyay (1), Hongbin Weng (1)

Institutions:

(1) U.S. Energy Information Administration, N/A, (2) DOE/EIA/SMG, N/A

Co-Author(s):

Preston McDowney  
DOE/EIA/SMG
Pushpal Mukhopadhyay  
U.S. Energy Information Administration
Hongbin Weng  
U.S. Energy Information Administration

First Author:

Makayla Cowles  
U.S. Energy Information Administration

Presenting Author:

Makayla Cowles  
N/A

Abstract Text:

In January 2022, the U.S. Energy Information Administration (EIA) launched a new census survey to collect natural gas inventory storage data from all operating liquefied natural gas (LNG) storage facilities in the U.S. The EIA-191L, Monthly Liquefied Natural Gas Storage Report, collects data on injections, withdrawals, total gas in storage, total capacity, and maximum delivery for operators of LNG facilities across 29 states. EIA uses these data to publish state‑level monthly estimates on LNG storage in EIA's Natural Gas Monthly. The data are also used in several other EIA publications such as the Natural Gas Annual, Monthly Energy Review, and Short-Term Energy Outlook.

To account for unit non-response in the 2022 survey, we developed a donor-based imputation method. It creates imputation cells using the monthly activities for the LNG facilities and selects donors based on the donors' expected total gas and the recipient's reported total gas for January 2023. In this presentation, we will discuss data quality metrics and statistical methodologies used in EIA-191L, emphasizing statistical editing and imputation methods.

Keywords:

Energy Statistics|Clustering| | | |

Sponsors:

Survey Research Methods Section

Tracks:

Missing Data Methods/Non-response Bias Analysis

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