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What are the most common smart metering data quality issues?

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Smart meters are devices that measure and record the consumption of electricity, gas, or water at regular intervals and transmit the data to a central system. They are widely used in energy management projects to improve efficiency, reduce costs, and support renewable energy integration. However, smart metering data quality is a critical factor that can affect the accuracy, reliability, and usability of the information collected. In this article, you will learn about some of the most common smart metering data quality issues and how to prevent or resolve them.

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Shakir Shakoor K.

CEM? | Energy Engineer | Energy Management | Decarbonization | Sustainability | MSME (Georgia Tech) | Fulbright Alumnus
Alper üzmezler

Managing Partner @ BAS Services & Graphics, LLC | Sustainable Energy
Jose Rebollo Pericot

Mtr. Ingeniero de Caminos, C. y P, Consultor de Proyectos en Renovables y Obra Civil

1 Missing data

One of the most common data quality issues in smart metering is missing data, which occurs when the meter fails to record or transmit some or all of the readings for a certain period. Missing data can be caused by various factors, such as communication errors, network failures, power outages, meter malfunctions, or tampering. Missing data can lead to inaccurate billing, incomplete analysis, and reduced customer satisfaction. To prevent or minimize missing data, you should ensure that the meters are properly installed, maintained, and secured, and that the communication network is reliable and robust. You should also monitor the data regularly and use appropriate methods to fill in the gaps, such as interpolation, imputation, or estimation.

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Jose Rebollo Pericot

Mtr. Ingeniero de Caminos, C. y P, Consultor de Proyectos en Renovables y Obra Civil
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Hablar de "medición inteligente", es un enunciado que roza el oximorón. El dato es un número "desnudo", con una referencia espacio temporal (x,y,z,t) y unas unidades que lo cualifican como fenómeno físico, el que se quiere documentar y analizar, ahora si, inteligentemente. Y a este dato se le a?aden unidades S.I. para complementarlo. Es el procesamiento posterior el que debe ser inteligente. El rasgo de inteligencia en el procesamiento de datos es que se sabe prescindir del "ruido". ?O acaso calificamos cómo inteligentes los datos que son 100% útiles?, porque alguien ha planificado con anterioridad a la obtención del dato como era necesario medir. Yo diría que la inteligencia entonces no corresponde al "dato", sino al "observador".

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2 Inconsistent data

Another common data quality issue in smart metering is inconsistent data, which occurs when the meter records or transmits readings that do not match the expected or historical patterns. Inconsistent data can be caused by various factors, such as meter errors, calibration issues, environmental changes, customer behavior changes, or fraudulent activities. Inconsistent data can lead to misleading or erroneous results, such as overestimating or underestimating the consumption, demand, or savings. To prevent or detect inconsistent data, you should ensure that the meters are accurate, calibrated, and verified, and that the data is validated and verified against predefined rules, thresholds, or benchmarks. You should also analyze the data for outliers, anomalies, or trends, and investigate the causes and impacts of any deviations.

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Jose Rebollo Pericot

Mtr. Ingeniero de Caminos, C. y P, Consultor de Proyectos en Renovables y Obra Civil
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Los "datos incoherentes" en puridad no son datos. Es ruido. Elementos innecesarios que deben de ser eliminados porqué no corresponden a la observación que quiere analizarse "inteligentemente". Es la inteligencia del analista quién detecta la inutilidad para su uso posterior y debe de proceder a eliminarlos. Esto no es fácil, y pone a prueba la "inteligencia" del evaluador. Si no se sabe filtrar, ya no se sabrá analizar. Pero además es la constatación de que no se conoce el fenómeno físico o "realidad" a comprender. Parecía que el dato era condición única y necesaria para la comprensión "inteligente" del fenómeno, pero no es así. No sólo el "observador" importa para evitar la "Falta de Datos", también el "analista" importa para confirmarlos.

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Matt Landoe, CEM

Account Manager at ATS Inland NW (Building Automation Systems)
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When the data is very important, spending the extra money to buy communicating meters that store data in the meter rather than sending contact pulses to the controller (while being considerably more expensive) can really protect your critical meter data from losses and always ensure the DDC system matches the value of the meter.

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3 Incomplete data

Another common data quality issue in smart metering is incomplete data, which occurs when the meter records or transmits readings that do not cover the entire scope or range of the measurement. Incomplete data can be caused by various factors, such as meter limitations, configuration issues, sampling errors, or data aggregation or disaggregation issues. Incomplete data can lead to incomplete or biased insights, such as missing or overlooking some aspects or segments of the consumption, demand, or savings. To prevent or address incomplete data, you should ensure that the meters are capable, compatible, and configured to capture and transmit the relevant and sufficient data for your project objectives and requirements. You should also ensure that the data is properly sampled, aggregated, or disaggregated to reflect the appropriate level of detail and granularity.

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Alper üzmezler

Managing Partner @ BAS Services & Graphics, LLC | Sustainable Energy
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We use Project Haystack (project-haystack.org) to maintain consistency and clarity in data. It employs semantic modeling to define what the loads are, how long the data has trended, and the methods used for trending. This approach ensures data is self-explanatory and reliable. Inconsistent data in smart metering, often due to meter errors, calibration issues, or environmental changes, can result in inaccurate consumption or savings estimates. To address this, meters should be accurately calibrated and verified. Data should be validated against established rules or benchmarks. Regular analysis for outliers and trends, coupled with investigations into any deviations, helps maintain data quality and integrity.

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Jose Rebollo Pericot

Mtr. Ingeniero de Caminos, C. y P, Consultor de Proyectos en Renovables y Obra Civil
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La constatación de la "incompletitud" de los datos la detecta el "analista" en su procesamiento "inteligente" y es el quien requiere un nuevo bucle de obtención de datos. En este momento el "analista procesador" pasa a convertirse en "observador" y vuelve a la casilla de salida, para enfrentarse a la "falta de datos" y volver a dise?ar un proceso de obtención de datos en el que no se de esta circunstancia y poder proceder al "procesamiento inteligente" de los datos.

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4 Incorrect data

Another common data quality issue in smart metering is incorrect data, which occurs when the meter records or transmits readings that do not reflect the actual or true consumption, demand, or savings. Incorrect data can be caused by various factors, such as meter faults, measurement errors, conversion errors, or data manipulation or corruption. Incorrect data can lead to incorrect or false conclusions, such as overclaiming or underclaiming the consumption, demand, or savings. To prevent or correct incorrect data, you should ensure that the meters are functioning, measuring, and converting the data correctly and consistently, and that the data is securely stored, transmitted, and processed. You should also check the data for accuracy, consistency, and integrity, and use appropriate methods to correct or adjust any errors, such as recalibration, reconciliation, or adjustment.

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Jose Rebollo Pericot

Mtr. Ingeniero de Caminos, C. y P, Consultor de Proyectos en Renovables y Obra Civil
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Los "datos incorrectos", son casos particulares de "datos incoherentes" que deben desecharse, o de "datos incompletos" que deben de complementarse, volviendo al inicio del proceso.

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5 Duplicate data

Another common data quality issue in smart metering is duplicate data, which occurs when the meter records or transmits the same or similar readings more than once for the same or overlapping periods. Duplicate data can be caused by various factors, such as meter reboots, communication retries, data retransmission, or data duplication or replication. Duplicate data can lead to redundant or inflated results, such as double-counting or over-reporting the consumption, demand, or savings. To prevent or eliminate duplicate data, you should ensure that the meters are stable, synchronized, and coordinated, and that the communication network is efficient and effective. You should also identify and remove any duplicate or redundant data from the data set, using appropriate methods such as deduplication, filtering, or consolidation.

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Jose Rebollo Pericot

Mtr. Ingeniero de Caminos, C. y P, Consultor de Proyectos en Renovables y Obra Civil
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Los "datos duplicados" son copias y no deben de confundirse con datos redundantes o de seguridad. Las "copias" sólo deben de procesarse en tanto que datos originales, una única vez. Es un caso particular de "ruido" de más fácil detección que los "datos incoherentes".

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Shakir Shakoor K.

CEM? | Energy Engineer | Energy Management | Decarbonization | Sustainability | MSME (Georgia Tech) | Fulbright Alumnus
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Even after resolving calibration and network-related issues, the data acquired using smart meters may contain duplicate values or inconsistent values. Therefore, an energy manager/auditor should always perform correlation analysis to find the outliers in the data. Also, cleaning the data using suitable tools (Python or R) for duplicate values and filling in missing values is important to perform further analysis.

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Jonathan Henderson

Technical Services | Sustainability | Energy Efficiency | Net Zero
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The data is only as good as you decide to do with it. I've seen dozens, hundreds, perhaps of reports that are unanswered or unnoticed as the operation simply doesn't have the knowledge or staff to make the needed changes.

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Mitch Kennedy

Sustainability Director @ FactoryoftheFuture.org | Energy Resilience Sustainability and Supply Chain Hardening
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Wireless data gathering systems can interfere with other comms in large facilities, and sometimes people can't resist touching something new they haven't seen in their space before. Mount loggers high, post a sign, and spread the word that these strange objects will be around for a week/month, whatever. Better to tell everyone on Friday, than to find it smashed on the ground Monday morning.

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What are the most common smart metering data quality issues?

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2

3

4

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1 Missing data

2 Inconsistent data

3 Incomplete data

4 Incorrect data

5 Duplicate data

6 Here’s what else to consider

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