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[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.We study a problem faced by PT. Perusahaan Gas Negara (abbreviated as PGN), the Indonesian company in the natural gas transportation and distribution segment, in developing data reconciliation system for billing process. PGN serves its residential, commercial and industrial customers’ natural gas need through transmission pipelines. We focus our study on the billing process for industrial customers, where gas consumptions are measured by combining measurement results from turbine meter, temperature measurement device and pressure measurement device. Normally, customers’ bills can be obtained directly from volume metered by volume measurement device. But in every month there are a number of anomaly behaviour in customers’ gas consumption data or reports on meter failure, although statistically there are less than 1% of meter failure or anomaly behaviour from all customers’ data. Due to the large number of on-site meters which located in very wide area in Sumatera and Java islands, sometimes defective meter problems are already resolved after several days since the problem was reported/indicated, and this can generate loss for PGN. In this paper, we perform analysis to answer: 1. how to determine the beginning of the period where a volume meter is fail, 2. how to estimate gas volume delivered to the customers during the meter failure. The first problem is solved by analysing historical volume, temperature, and pressure data of each customer. The analysis results, in line with Spitzglass (Energy) Equation, that gas flow is linear to the pressure drop between the pressure at distribution pipe and the pressure at the customer’s pipe. We derive a procedure for detecting anomaly in gas volume data based on this phenomenon, where we reconcile the volume data and the pressure data and categorize the reconciled data into normal type or anomaly type data. The period during the meter failure is then determined as the period where the data in this period are of anomaly type. As soon as the period during the meter failure is determined, we then need to estimate the gas volume delivered during this period. We apply some forecasting methods for time series data such as AR(p) and Holt Winters models, and we choose the estimation from the forecasting model with the smallest error. The solution of those problems will be used for calculating bills for those customers which experience meter defective in the billing month. We develop a graphical user interface based on the anomaly detection method and the forecasting method. This system has been implemented, tested, and used by Energy Management Division of PGN, and will be developed into an energy management system.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Anomaly detection methods,Data reconciliation,Forecasting methods,Forecasting modeling,Industrial customer,Management division,Pressure measurement devices,Through transmission[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1757-899X/567/1/012002[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]