which are therefore limited by feature selection and analyzing pattern.

Introduction The dataset presented shows import consumption data at a low voltage (LV) feeder level, together with a count of smart meters contributing the aggregated half-hourly values. The data shown now includes values for both Active Energy import and Reactive. This dataset is currently from 40,000 smart meters in our Eastern Power Networks (EPN) region. This will be gradually expanded during 2025 to include both import and export energy data from all smart meters within our three regions of EPN, London Power Networks (LPN), and South Eastern Power Networks (SPN).

Methodological Approach
Primary Consumption Active Import is aggregated from the number of active devices during a half hour period. Note: if a device was not contactable during a half period, its data is not aggregated.Quality Control Statement Please be aware that this data is being made available to provide an early insight of what we propose to publish from all smart meters within our regions. We continue to carry out data validation checks to improve data quality before publishing the full dataset - so suggest caution if you plan to utilize this currently available information. We will update this message once the full dataset that includes both import and export energy data is published. Please provide any thoughts, comments, or suggestions here.

Assurance Statement The Smart Metering Team has checked to ensure data accuracy and consistency.

Other Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: Open Data Portal Glossary Download dataset information: Metadata (JSON)

Description: This dataset contains smart meter electricity consumption data enriched with weather conditions, historical consumption statistics, and anomaly labels for detecting unusual electricity usage patterns. It is designed for anomaly detection, predictive modeling, and energy consumption analysis using advanced machine learning techniques.

Key Features: Timestamp: 30-minute interval electricity consumption records. Electricity Consumed (kWh): Power usage per time interval. Temperature (°C): External temperature affecting consumption. Humidity (%): Air humidity levels. Wind Speed (km/h): Wind conditions influencing energy needs. Avg Past Consumption (kWh): Rolling average of past power usage. Anomaly Label: Normal or abnormal usage, detected using Isolation Forest. Use Cases: Anomaly Detection: Identify fraudulent or unusual electricity consumption. Energy Efficiency Analysis: Understand consumption trends and optimize energy use. Predictive Modeling: Train AI models for forecasting electricity demand. Smart Grid Management: Improve grid stability and power distribution. This dataset is ideal for machine learning researchers, data scientists, and energy analysts developing predictive models for real-time anomaly detection and energy optimization.

Cleaned Swiss smart meter data based on a collection from CKW AG (see opendata.ckw.ch)

Duration: 2 years (1. Jan 2021 - 31. Dec 2022, CET timestamp)

Location: Canton Lucerne, Switzerland

Interval: 15 minutes

Values: Active Energy (kWh)

Meters in each year: 4959 (see filtered_IDs.csv for all IDs)

The original dataset has been filtered based

on missing data

This means, all 4959 meters have consumption and reported values over the full duration of 2 years. Files are available as space-saving parquet files per day in year. Number in filename is number of day within the year.

Summary.zip contains summary statistics over all 112148 (unfiltered) meters counting observations (including duplicates) , and aggregating energy data (per month, and or hourly data), see overview.csv within summary.zip

Smart Meter Data Management Market Outlook

The smart meter data management market size is on a robust growth trajectory, with a global valuation of approximately USD 1.2 billion in 2023 and projected to reach an estimated USD 3.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.5% over the forecast period. This impressive growth is driven by the escalating adoption of smart meters across utilities worldwide, as energy providers and governments increasingly emphasize the importance of efficient energy consumption and management. The integration of advanced data analytics and machine learning into smart meter data management solutions is further fueling this expansion, allowing for enhanced optimization and predictive maintenance, thus bolstering the marketÂ’s growth prospects.

One of the key growth factors for the smart meter data management market is the global push towards energy efficiency and the reduction of carbon footprints. Governments across the world are implementing stringent regulatory frameworks that mandate the adoption of smart metering technologies to monitor and manage energy usage in real-time. This is particularly prevalent in regions like Europe and North America, where environmental sustainability is a high priority. Moreover, the increasing demand for real-time energy monitoring and the ability to remotely manage and adjust energy consumption profiles are compelling utilities to invest in sophisticated data management systems. These systems empower energy providers to offer more personalized energy solutions, resulting in greater customer satisfaction and subsequently driving market growth.

The rapid technological advancements and innovations in the Internet of Things (IoT) and cloud computing are further catalyzing the growth of the smart meter data management market. The integration of IoT with smart meters facilitates seamless data capture and transmission, enabling utilities to gather vast amounts of data from millions of endpoints. This data, when managed effectively, can yield valuable insights into consumer behavior and energy usage patterns, enabling utilities to devise more effective energy distribution strategies. Moreover, the adoption of cloud-based platforms for data management offers scalability and cost-efficiency, providing utilities with the flexibility to manage data at a lower infrastructure cost while ensuring data security and compliance with regulatory standards.

Another significant driver for the market is the rising investment in smart grid infrastructure, especially in emerging economies. Countries in Asia Pacific and Latin America are investing heavily in modernizing their energy infrastructure to support the growing population and urbanization. Smart meters are a critical component of smart grid projects, and their deployment necessitates robust data management solutions. The increased focus on renewable energy sources and the integration of distributed energy resources (DERs) into the grid further accentuate the need for advanced data management systems to ensure reliable and efficient energy distribution. This trend is expected to continue, driving substantial growth in the smart meter data management market over the coming years.

The role of IoT in Energy Grid Management is becoming increasingly pivotal as utilities seek to enhance the efficiency and reliability of their energy distribution networks. By leveraging IoT technologies, energy providers can achieve real-time monitoring and control over grid operations, leading to improved demand response and reduced energy wastage. IoT devices, such as sensors and smart meters, facilitate seamless communication between different components of the grid, enabling utilities to detect anomalies and optimize energy flows. This integration not only supports the efficient management of energy resources but also enhances the resilience of the grid against disruptions. As the energy sector continues to evolve, the adoption of IoT in energy grid management is expected to play a crucial role in driving the transition towards smarter and more sustainable energy systems.

Component Analysis

The smart meter data management market is segmented by component into software and services, each playing a crucial role in the market's development. Software solutions are essential for the collection, analysis, and interpretation of vast amounts of data generated by smart meters. These solutions include meter data management systems (MDMS) that offer functionalities such as data

Overview

The CKW Group is a distribution system operator that supplies more than 200,000 end customers in Central Switzerland. Since October 2022, CKW publishes anonymised and aggregated data from smart meters that measure electricity consumption in canton Lucerne. This unique dataset is accessible in the ckw.ch/opendata platform.

Data set A - anonimised smart meter data

Data set B - aggregated smart meter data

Contents of this data set

This data set contains a small sample of the CKW data set A sorted per smart meter ID, stored as parquet files named with the id field of the corresponding smart meter anonymised data. Example: 027ceb7b8fd77a4b11b3b497e9f0b174.parquet

The orginal CKW data is available for download at https://open.data.axpo.com/%24web/index.html#dataset-a as a (gzip-compressed) csv files, which are are split into one file per calendar month. The columns in the files csv are:

id: the anonymized counter ID (text)

timestamp: the UTC time at the beginning of a 15-minute time window to which the consumption refers (ISO-8601 timestamp)

value_kwh: the consumption in kWh in the time window under consideration (float)

In this archive, data from:

| Dateigrösse | Export Datum | Zeitraum | Dateiname || ----------- | ------------ | -------- | --------- || 4.2GiB | 2024-04-20 | 202402 | ckw_opendata_smartmeter_dataset_a_202402.csv.gz || 4.5GiB | 2024-03-21 | 202401 | ckw_opendata_smartmeter_dataset_a_202401.csv.gz || 4.5GiB | 2024-02-20 | 202312 | ckw_opendata_smartmeter_dataset_a_202312.csv.gz || 4.4GiB | 2024-01-20 | 202311 | ckw_opendata_smartmeter_dataset_a_202311.csv.gz || 4.5GiB | 2023-12-20 | 202310 | ckw_opendata_smartmeter_dataset_a_202310.csv.gz || 4.4GiB | 2023-11-20 | 202309 | ckw_opendata_smartmeter_dataset_a_202309.csv.gz || 4.5GiB | 2023-10-20 | 202308 | ckw_opendata_smartmeter_dataset_a_202308.csv.gz || 4.6GiB | 2023-09-20 | 202307 | ckw_opendata_smartmeter_dataset_a_202307.csv.gz || 4.4GiB | 2023-08-20 | 202306 | ckw_opendata_smartmeter_dataset_a_202306.csv.gz || 4.6GiB | 2023-07-20 | 202305 | ckw_opendata_smartmeter_dataset_a_202305.csv.gz || 3.3GiB | 2023-06-20 | 202304 | ckw_opendata_smartmeter_dataset_a_202304.csv.gz || 4.6GiB | 2023-05-24 | 202303 | ckw_opendata_smartmeter_dataset_a_202303.csv.gz || 4.2GiB | 2023-04-20 | 202302 | ckw_opendata_smartmeter_dataset_a_202302.csv.gz || 4.7GiB | 2023-03-20 | 202301 | ckw_opendata_smartmeter_dataset_a_202301.csv.gz || 4.6GiB | 2023-03-15 | 202212 | ckw_opendata_smartmeter_dataset_a_202212.csv.gz || 4.3GiB | 2023-03-15 | 202211 | ckw_opendata_smartmeter_dataset_a_202211.csv.gz || 4.4GiB | 2023-03-15 | 202210 | ckw_opendata_smartmeter_dataset_a_202210.csv.gz || 4.3GiB | 2023-03-15 | 202209 | ckw_opendata_smartmeter_dataset_a_202209.csv.gz || 4.4GiB | 2023-03-15 | 202208 | ckw_opendata_smartmeter_dataset_a_202208.csv.gz || 4.4GiB | 2023-03-15 | 202207 | ckw_opendata_smartmeter_dataset_a_202207.csv.gz || 4.2GiB | 2023-03-15 | 202206 | ckw_opendata_smartmeter_dataset_a_202206.csv.gz || 4.3GiB | 2023-03-15 | 202205 | ckw_opendata_smartmeter_dataset_a_202205.csv.gz || 4.2GiB | 2023-03-15 | 202204 | ckw_opendata_smartmeter_dataset_a_202204.csv.gz || 4.1GiB | 2023-03-15 | 202203 | ckw_opendata_smartmeter_dataset_a_202203.csv.gz || 3.5GiB | 2023-03-15 | 202202 | ckw_opendata_smartmeter_dataset_a_202202.csv.gz || 3.7GiB | 2023-03-15 | 202201 | ckw_opendata_smartmeter_dataset_a_202201.csv.gz || 3.5GiB | 2023-03-15 | 202112 | ckw_opendata_smartmeter_dataset_a_202112.csv.gz || 3.1GiB | 2023-03-15 | 202111 | ckw_opendata_smartmeter_dataset_a_202111.csv.gz || 3.0GiB | 2023-03-15 | 202110 | ckw_opendata_smartmeter_dataset_a_202110.csv.gz || 2.7GiB | 2023-03-15 | 202109 | ckw_opendata_smartmeter_dataset_a_202109.csv.gz || 2.6GiB | 2023-03-15 | 202108 | ckw_opendata_smartmeter_dataset_a_202108.csv.gz || 2.4GiB | 2023-03-15 | 202107 | ckw_opendata_smartmeter_dataset_a_202107.csv.gz || 2.1GiB | 2023-03-15 | 202106 | ckw_opendata_smartmeter_dataset_a_202106.csv.gz || 2.0GiB | 2023-03-15 | 202105 | ckw_opendata_smartmeter_dataset_a_202105.csv.gz || 1.7GiB | 2023-03-15 | 202104 | ckw_opendata_smartmeter_dataset_a_202104.csv.gz || 1.6GiB | 2023-03-15 | 202103 | ckw_opendata_smartmeter_dataset_a_202103.csv.gz || 1.3GiB | 2023-03-15 | 202102 | ckw_opendata_smartmeter_dataset_a_202102.csv.gz || 1.3GiB | 2023-03-15 | 202101 | ckw_opendata_smartmeter_dataset_a_202101.csv.gz |

was processed into partitioned parquet files, and then organised by id into parquet files with data from single smart meters.

A small sample of all the smart meters data above, are archived in the cloud public cloud space of AISOP project https://os.zhdk.cloud.switch.ch/swift/v1/aisop_public/ckw/ts/batch_0424/batch_0424.zip and also here is this public record. For access to the complete data contact the authors of this archive.

It consists of the following parquet files:

| Size | Date | Name |

|------|------|------|

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| 1.0M | Mar 4 12:18 | 027b7b950689c337d311094755697a8f.parquet |

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where each file contains data from a single smart meter.

Acknowledgement

The AISOP project (https://aisopproject.com/) received funding in the framework of the Joint Programming Platform Smart Energy Systems from European Union's Horizon 2020 research and innovation programme under grant agreement No 883973. ERA-Net Smart Energy Systems joint call on digital transformation for green energy transition.

Global Smart Meter Data Management market size is expected to reach $5.07 billion by 2029 at 19.4%, segmented as by software, data analytics software, meter data management software, billing and customer information software, visualization software

All DNOs are required by licence condition to comply with Ofgem’s Data Best Practice (DBP) Guidance. The DBP Guidance was updated by Ofgem on 7th August 2023, and the DBP Guidance now requires DNOs to openly publish Aggregated Smart Meter Consumption Data from 28th February 2024. This data set contains Aggregated Smart Meter Consumption Data at a Distribution Secondary Substations level. Presented in gz format due to file size considerations. If you're using Windows, you can extract GZ files using the "tar" command in Command Prompt or by installing the 7-Zip program. On a Mac, just double-click the file to extract it, or use the command gunzip filename. gz in a Terminal window. If you're using Linux, use the gzip -d filename.

Introduction The dataset presented shows import consumption data at secondary substation level, together with a count of smart meters contributing the aggregated half-hourly values. The data shown now includes values for both Active Energy import and Reactive. This dataset is currently from 40,000 smart meters in our Eastern Power Networks (EPN) region. This will be gradually expanded during 2025 to include both import and export energy data from all smart meters within our three regions of EPN, London Power Networks (LPN), and South Eastern Power Networks (SPN).

Methodological Approach
Primary Consumption Active Import is aggregated from the number of active devices during a half hour period. Note: if a device was not contactable during a half period, its data is not aggregated.Quality Control Statement Please be aware that this data is being made available to provide an early insight of what we propose to publish from all smart meters within our regions. We continue to carry out data validation checks to improve data quality before publishing the full dataset - so suggest caution if you plan to utilize this currently available information. We will update this message once the full dataset that includes both import and export energy data is published. Please provide any thoughts, comments, or suggestions here.

Assurance Statement The Smart Metering Team has checked to ensure data accuracy and consistency. Other Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: Open Data Portal Glossary Download dataset information: Metadata (JSON)

Name: GoiEner smart meters data Summary: The dataset contains hourly time series of electricity consumption (kWh) provided by the Spanish electricity retailer GoiEner. The time series are arranged in four compressed files: raw.tzst, contains raw time series of all GoiEner clients (any date, any length, may have missing samples). imp-pre.tzst, contains processed time series (imputation of missing samples), longer than one year, collected before March 1, 2020. imp-in.tzst, contains processed time series (imputation of missing samples), longer than one year, collected between March 1, 2020 and May 30, 2021. imp-post.tzst, contains processed time series (imputation of missing samples), longer than one year, collected after May 30, 2020. metadata.csv, contains relevant information for each time series. License: CC-BY-SA Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943. Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein. Collection Date: From November 2, 2014 to June 8, 2022. Publication Date: December 1, 2022. DOI: 10.5281/zenodo.7362094 Other repositories: None. Author: GoiEner, University of Deusto. Objective of collection: This dataset was originally used to establish a methodology for clustering households according to their electricity consumption. Description: The meaning of each column is described next for each file. raw.tzst: (no column names provided) timestamp; electricity consumption in kWh. imp-pre.tzst, imp-in.tzst, imp-post.tzst: “timestamp”: timestamp; “kWh”: electricity consumption in kWh; “imputed”: binary value indicating whether the row has been obtained by imputation. metadata.csv: “user”: 64-character identifying a user; “start_date”: initial timestamp of the time series; “end_date”: final timestamp of the time series; “length_days”: number of days elapsed between the initial and the final timestamps; “length_years”: number of years elapsed between the initial and the final timestamps; “potential_samples”: number of samples that should be between the initial and the final timestamps of the time series if there were no missing values; “actual_samples”: number of actual samples of the time series; “missing_samples_abs”: number of potential samples minus actual samples; “missing_samples_pct”: potential samples minus actual samples as a percentage; “contract_start_date”: contract start date; “contract_end_date”: contract end date; “contracted_tariff”: type of tariff contracted (2.X: households and SMEs, 3.X: SMEs with high consumption, 6.X: industries, large commercial areas, and farms); “self_consumption_type”: the type of self-consumption to which the users are subscribed; “p1”, “p2”, “p3”, “p4”, “p5”, “p6”: contracted power (in kW) for each of the six time slots; “province”: province where the user is located; “municipality”: municipality where the user is located (municipalities below 50.000 inhabitants have been removed); “zip_code”: post code (post codes of municipalities below 50.000 inhabitants have been removed); “cnae”: CNAE (Clasificación Nacional de Actividades Económicas) code for economic activity classification. 5 star: ⭐⭐⭐ Preprocessing steps: Data cleaning (imputation of missing values using the Last Observation Carried Forward algorithm using weekly seasons); data integration (combination of multiple SIMEL files, i.e. the data sources); data transformation (anonymization, unit conversion, metadata generation). Reuse: This dataset is related to datasets: "A database of features extracted from different electricity load profiles datasets" (DOI 10.5281/zenodo.7382818), where time series feature extraction has been performed. "Measuring the flexibility achieved by a change of tariff" (DOI 10.5281/zenodo.7382924), where the metadata has been extended to include the results of a socio-economic characterization and the answers to a survey about barriers to adapt to a change of tariff. Update policy: There might be a single update in mid-2023. Ethics and legal aspects: The data provided by GoiEner contained values of the CUPS (Meter Point Administration Number), which are personal data. A pre-processing step has been carried out to replace the CUPS by random 64-character hashes. Technical aspects: raw.tzst contains a 15.1 GB folder with 25,559 CSV files; imp-pre.tzst contains a 6.28 GB folder with 12,149 CSV files; imp-in.tzst contains a 4.36 GB folder with 15.562 CSV files; and imp-post.tzst contains a 4.01 GB folder with 17.519 CSV files. Other: None.

Market Overview: The global smart meter data management market is projected to reach a valuation of $730.5 million by 2033, growing at a CAGR of 12.6% during the forecast period (2025-2033). Factors contributing to this growth include increasing energy consumption, government initiatives to promote energy efficiency, and technological advancements in smart metering infrastructure. The market is segmented by type (softwares and services) and application (public infrastructure, energy development, power generation, and others). Drivers, Trends, and Challenges: The adoption of smart meters to monitor and manage energy consumption is a key driver for this market. Smart meters provide real-time data, enabling utilities and consumers to identify and optimize energy usage patterns. Additionally, government regulations and incentives related to smart grid infrastructure, data security, and privacy concerns are shaping the market. However, challenges such as data integration and management, cybersecurity risks, and limited interoperability among different vendors can hinder growth. Smart Meter Data Management This report provides comprehensive insights into the global Smart Meter Data Management market, highlighting its key trends, growth drivers, challenges, and the competitive landscape.

A survey conducted in 2024 found that 41 percent of female respondents used a smart meter to monitor their energy usage. When comparing smart meter use by highest qualification, the survey found that 43 respondents with a degree or higher had a smart meter at home. In terms of UK smart meter usage by age, 35-44 year-olds were the most likely to have installed such an electric meter.

In this article, the 3 sets of Real European test feeders: industrial which is integrated medium and low voltage, rural low voltage, and urban low voltage networks are proposed by using real data of GIS and smart meter readings obtained from a distribution company. The authors provide the real mathematical OpenDSS model of the three standards as master files with their corresponding real smart meter readings and topological data in database.

Each of the three proposed networks has different network features. The industrial network is comprised of both medium and low voltage areas with 2888 nodes, 777 buses, and 556 lines. It addresses 165 low voltage and 26 medium voltage industrial customers using 22 distribution transformers. In the rural network, there are 18599 nodes, 4650 buses, and 4291 lines to supply 2731 end customers. While 26951 nodes, 6738 buses, and 5905 lines are found in the urban network that electrifies 35297 low voltage customers. For rural and urban networks 68 distribution transformers are used in each of the networks to address their customers with both single and three phase systems.

The movement of decarbonization leads to comprise several advanced and smart devices at electricity society and enhancing the application demand response systems. Mainly, deployment of different flexible devices such as EV, heat pump, distribution generation in the distribution system takes the existing system to higher level of complication. Hence, that drives distribution grid system to enter to revolutionary transition which is digitalization of the system, to enable real time management of distribution system as it is undergoing through huge complexity. Such systems requires real mathematical model of distribution network therefore this three different test cases are developed. Majority of the existing test systems are synthetic and not representing the real system of the European network. In addition to being limited quantitative wise and for a specific problem solving, their is a lack of integrated real European testcase which incorporates both the low voltage and medium voltage networks. To fill the gap authors develop the test feeders that address industrial, rural and urban areas which is significantly important for researchers. Here, the corresponding OpenDSS model and demand profiles extracted from smart meters of each standards archived in their 'Master' and 'PQ_csv' folders, respectively. In addition, their topological data is provided in their associated databases. The detail description about the data set and all the development are contained in a paper with the same title of the dataset that it is under review and will be linked to this dataset.

According to Cognitive Market Research, the global Smart meter data management market size will be USD 1565.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 18.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 626.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 469.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 360.00 million in 2024 and will grow at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 78.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 31.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.9% from 2024 to 2031.
The software held the highest Smart meter data management market revenue share in 2024.

Market Dynamics of Smart meter data management Market

Key Drivers for Smart meter data management Market

Utility industry transformations to increase the demand globally

The utility industry is undergoing significant transformations driven by the need for increased efficiency, sustainability, and customer engagement. Innovations in smart grid technologies, data analytics, and renewable energy integration are reshaping how utilities operate. The adoption of smart meters and advanced data management systems enhances real-time monitoring and decision-making, enabling more efficient resource distribution and improved customer service. Regulatory pressures and global sustainability goals further accelerate this shift, pushing utilities towards greener practices and smarter infrastructure. These changes are expanding market opportunities globally, as utilities and consumers alike seek to optimize energy use and reduce environmental impact.

Increased demand for energy efficiency to propel market growth

The growing demand for energy efficiency is significantly propelling market growth for smart meter data management systems. As energy costs rise and environmental concerns intensify, both consumers and utilities are increasingly prioritizing energy-saving measures. Smart meters provide real-time data on energy consumption, enabling more precise management and optimization. This data helps identify inefficiencies, reduce waste, and support targeted conservation efforts. Consequently, the focus on improving energy efficiency drives the adoption of advanced smart metering solutions, which offer enhanced monitoring, analysis, and control capabilities. This heightened awareness and need for efficiency fuel market expansion and innovation in energy management technologies.

Restraint Factor for the Smart meter data management Market

Operational disruptions to limit the sales

Operational disruptions can significantly limit sales in the smart meter data management market. Implementing new technologies often requires extensive system integration and adaptation, which can interrupt existing processes and workflows. These disruptions may lead to temporary inefficiencies, increased costs, and resistance from staff and stakeholders. Additionally, the transition phase might involve steep learning curves and potential technical issues, further complicating deployment. Such challenges can delay or deter organizations from adopting smart meter solutions, impacting overall sales. To mitigate these effects, companies must focus on seamless integration, comprehensive training, and robust support systems to minimize operational disruptions and maintain market momentum.

Impact of Covid-19 on the Smart meter data management Market

The COVID-19 pandemic negatively impacted the smart meter data management market, causing significant disruptions. Lockdowns and social distancing measures slowed down the installation and maintenance of smart meters, leading to delays in project timelines and reduced market activity. Economic uncertainties and budget constraints faced by utilities and businesses resulted in postponed or canceled investments in new technologies. Additionall...

This dataset contains half hourly smart meter measurements of 4443 households, obtained during the Low Carbon London project, during 2013.

It is a refactored version of the data released by UK Power Networks under CC-BY license. The following filters have been applied:

• Only readings for households on the 'Std' tariff (i.e., non-time-of-use) have been retained
• 'Null' readings have been dropped
• Only data pertaining to energy consumption in 2013 has been retained. The first measurement has time stamp '2013-01-01 00:30:00.0000000' and the final measurement has time stamp '2014-01-01 00:00:00.0000000'.

Description of the data format:

• Zipped CSV file (utf-8 encoding, comma deliminator)
• First column is DateTime (GMT; no adjustment for British Summer Time)
• First row contains household labels
• Measurements are in kWh (energy consumption) for the preceding half hour. Note that to generate power profiles (with interpretation as average power in kW per half hour) requires multiplication by two.

Note: a cleaner version of the same data set, accompanied by survey data, is available under a more restrictive license at DOI: http://doi.org/10.5255/UKDA-SN-7857-2.

Context

This high-frequency three minutes interval smart meter dataset provides information on urban household electricity consumption patterns from nearly 100 smart meters installed in Mathura and Bareilly districts of Uttar Pradesh, India from May 2019 to October 2021. Apart from this, the data also provides information on the situation of power supply (including hours of power supply, voltage, current withdrawn and other related variables). The data can have various use cases for researchers and practitioners working in the power sector domain. For instance, power distribution companies (discoms) can utilize the smart meter data for effective service delivery and demand management.

Content

8 csv files containing various rows of data

Description of files

• CEEW - Smart meter data Bareilly 2019.csv: A csv file that contains the information about electricity consumption (t_kWh), average voltage (z_Avg Voltage (Volt)), average current (z_Avg Current (Amp)), frequency (y_Freq (Hz)) of the smart meter(meter) installed in Bareilly at every three minute interval (x_Timestamp) for the year 2019 in csv formats.

• CEEW - Smart meter data Bareilly 2020.csv: A csv file that contains the information about electricity consumption (t_kWh), average voltage (z_Avg Voltage (Volt)), average current (z_Avg Current (Amp)), frequency (y_Freq (Hz)) of the smart meter(meter) installed in Bareilly at every three minute interval (x_Timestamp) for the year 2020 in csv formats.

• CEEW - Smart meter data Bareilly 2021.csv: A csv file that contains the information about electricity consumption (t_kWh), average voltage (z_Avg Voltage (Volt)), average current (z_Avg Current (Amp)), frequency (y_Freq (Hz)) of the smart meter(meter) installed in Bareilly at every three-minute interval (x_Timestamp) for the year 2021 in csv formats.

• CEEW - Smart meter data Bareilly Aggregated.csv: A csv file that contains the information about daily electricity consumption (t_kWh) of the smart meter(meter) installed in Bareilly for the year 2019,2020 and 2021 in csv formats.

• CEEW - Smart meter data Mathura 2019.csv: A csv file that contains the information about electricity consumption (t_kWh), average voltage (z_Avg Voltage (Volt)), average current (z_Avg Current (Amp)), frequency (y_Freq (Hz)) of the smart meter(meter) installed in Mathura at every three-minute interval (x_Timestamp) for the year 2019 in csv formats.

• CEEW - Smart meter data Mathura 2020.csv: A csv file that contains the information about electricity consumption (t_kWh), average voltage (z_Avg Voltage (Volt)), average current (z_Avg Current (Amp)), frequency (y_Freq (Hz)) of the smart meter(meter) installed in Mathura at every three-minute interval (x_Timestamp) for the year 2020 in csv formats.

• CEEW - Smart meter data Mathura 2021.csv: A csv file that contains the information about electricity consumption (t_kWh), average voltage (z_Avg Voltage (Volt)), average current (z_Avg Current (Amp)), frequency (y_Freq (Hz)) of the smart meter(meter) installed in Mathura at every three-minute interval (x_Timestamp) for the year 2021 in csv formats.

• CEEW - Smart meter data Mathura Aggregated.csv: A csv file that contains the information about daily electricity consumption (t_kWh) of the smart meter(meter) installed in Mathura for the year 2019,2020 and 2021 in csv formats.

****Acknowledgements****

Authors

1. Agrawal, Shalu (Council on Energy, Environment and Water)
2. Mani, Sunil (Council on Energy, Environment and Water)
3. Ganesan, Karthik (Council on Energy, Environment and Water)
4. Jain, Abhishek (Council on Energy, Environment and Water)

url=https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GOCHJH

doi=doi:10.7910/DVN/GOCHJH

In response to the increasing necessity for accurate campus electricity management, understanding load patterns is essential for enhancing energy efficiency and optimizing usage. Yet, comprehensive electricity load data for campus buildings and their internal systems is often insufficient, posing challenges for research. This paper presents an energy consumption monitoring dataset from the Hong Kong University of Science and Technology (HKUST) campus, featuring data from over 1,400 meters across more than 20 buildings, collected over two and a half years. Utilizing the Brick Schema curation strategy, raw data was refined into a research-ready format. This dataset facilitates a variety of research applications, including load pattern recognition, fault detection, demand response strategies, and load forecasting.

Dataset content available to registered users only

ENWL Smart Meter Installation

Dataset showing the installed Smart Meter penetration levels, aggregated and shown as a percentage of the total customers connected to Low Voltage Network from any one Distribution Substation.

Data is recorded for those ENWL owned Distribution Substations with with 5 or more customers connected to the Low Voltage Network

While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information.

If you have any other query related to the ENWL Smart Meter Installation data, please contact us

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All DNOs are required by licence condition to comply with Ofgem’s Data Best Practice (DBP) Guidance. The DBP Guidance was updated by Ofgem on 7th August 2023, and the DBP Guidance now requires DNOs to openly publish Aggregated Smart Meter Consumption Data from 28th February 2024. This data set contains Aggregated Smart Meter Consumption Data at a Low Voltage Feeder level. Presented in gz format due to file size considerations. If you're using Windows, you can extract GZ files using the "tar" command in Command Prompt or by installing the 7-Zip program. On a Mac, just double-click the file to extract it, or use the command gunzip filename. gz in a Terminal window. If you're using Linux, use the gzip -d filename.

Benchmarking Smart Meter Data Analytics