In 2023, the number of residential smart meters in the United States amounted to 112 million, up from less than 40 millions in 2012. This trend suggests a growing awareness and adoption of smart meter technology among American households. Energy costs on the rise This increase in smart meter usage may be due in part to rising energy costs around the world. While the United States has not been hit by rising energy costs in the same way as many European countries, it has not been immune to the global fuel shortage in the early 2020s. Despite having a strong domestic energy sector, the price of electricity in the U.S. increased by four percent in January 2025. Additionally, the consumer price index for energy in the U.S. shot up to 136 in 2024. These cost increases prompt consumers to investigate how to save on their energy bills. Smart meters can help households track their energy usage and identify areas where they can make changes to reduce their energy consumption. The energy crisis in Europe Energy costs have also been a concern in Europe, where many countries are heavily reliant on Russian energy imports. Energy price hikes have hit the European countries particularly hard, where wholesale electricity prices have surpassed 400 euros per megawatt-hour in 2022. In 2023, the share of households equipped with a smart meter in Europe ranged between 100 percent in Spain, Italy, and the Nordic countries and one percent in Germany.

Introduction This dataset presents import aggregated consumption data from Smart Meter customers at the secondary substation and LV Feeder level, along with the count of smart meters contributing to the aggregated half-hourly values. It includes both Active Energy Import and Reactive Energy Import readings.The sample comprises 10,000 records drawn from aggregated smart meter data across our three operational regions: Eastern Power Networks (EPN), London Power Networks (LPN), and South Eastern Power Networks (SPN).

Methodological Approach
Primary consumption data for Active Energy Import is aggregated based on the number of active devices reporting during each half-hour period. If a device is unreachable during a given interval, its data is excluded from the aggregation to maintain accuracy.Quality Control Statement This dataset is being shared to provide an early preview of the type of data we intend to publish from all smart meters within our regions. We are conducting monthly validation checks to enhance data quality prior to releasing the full dataset. Users are advised to exercise caution when interpreting or utilizing this preliminary data.Assurance Statement The Smart Metering Team has reviewed the dataset to ensure consistency and accuracy in the presented data.

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)To view this data please register and login.

This dataset provides aggregated volumes of smart and non-smart meters connected to the National Grid Electricity Distribution network and includes SMETS1, SMETS2 and non smart meters. There are two main types of smart meters – the older models known as SMETS 1 (Smart Meter Equipment Technical Specifications) and the newer versions that were rolled out in 2018, known as SMETS 2. While SMETS2 meters offer many of the same benefits as SMETS1 meters, they contain the most up-to-date technology to help make life easier – especially if you ever want to switch supplier.

Smart Meter Market is Segmented by Product Type (Smart Electricity Meters, Smart Gas Meters, and More), Communication Technology (RF-Mesh Cellular (2G/3G/4G/5G/NB-IoT), Others), Component (Hardware, Software, Services), End User (Residential, Commercial, Industrial and Utilities), and by Geography. The Market Forecasts are Provided in Terms of Volume (Units).

Duke Energy is the leading electric utility based on the amount of smart meters installed in the U.S. in 2019. The North Carolina-based company installed *** million smart meters that year. Ranking second, NextEra Energy installed roughly *** million smart meters at that time. Some of the subsidiaries of the Florida-based company includes Florida Power & Light (FPL), NextEra Energy Resources, and Gulf Power Company.

Smart metering in the United States – additional information

Smart meters have enabled the integration of new technologies and innovation in the electricity market. The collection of data enabled by smart meters, or automated metering infrastructure, allows for customers to be served based on their true consumption habits. There were some **** million advanced meters installed in the United States as of 2019. The use of smart meters by electric utilities has allowed companies to provide enhanced outage restoration and improved distribution monitoring, as well as new customer services. Some states in the country have enacted policies that allow customers to opt out of smart meter installations, yet, very few customers have requested to do so.

Pacific Gas & Electric, an investor-owned electric utility headquartered in San Francisco, generated some **** billion U.S. dollars in revenue from their electric operating segment in 2020. The company is considered one of the top smart meter companies in the United States and had over *** million smart meters installed around the country as of 2019.

This graph shows the forecast installed base of smart meters worldwide from 2014 to 2020. In 2017, the installed base of smart electricity, gas and water meters is projected to rise to ***********.

A total of *** million smart meters were deployed by the end of 2022 in the United States. This is an increase of over 100 million units compared to 2007. Duke Energy and NextEra Energy were the leading electric utilities based on number of smart meters installed in the U.S.

Kaggle London Smart Meters dataset contains 5560 half hourly time series that represent the energy consumption readings of London households in kilowatt hour (kWh) from November 2011 to February 2014.

The original dataset contains missing values. They have been replaced by carrying forward the corresponding last observations (LOCF method).

The Europe Smart Meter Market Report is Segmented by Meter Type (Smart Electricity Meter, Smart Gas Meter, and Smart Water Meter), Communication Technology (Power-Line Communication, Radio Frequency, and More), Component (Hardware, Software and Analytics, and Services), End-User (Residential, Commercial, and Industrial), Phase (Single-Phase and Three-Phase), and Country. The Market Forecasts are Provided in Terms of Value (USD).

The United States Residential Smart Meters Market is Segmented by Type (Smart Gas Meter, Smart Water Meter Smart Electricity Meter). The Market Size and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

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 a sample from 10,000 smart meters each, within our three regions of Eastern Power Networks (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.

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)

Government incentives and investments for grid modernization have boosted sales for smart meter manufacturers over the past five years. Growth in new construction activity has hiked sales. Exports to Canada have been robust, propelled by Canadian government incentives and the benefits of the USMCA. Retaliatory tariffs from Canada pose potential challenges, possibly curbing future exports. Meanwhile, exports to Mexico have dwindled, largely because of the strong US dollar and Mexico's smart meter production capabilities. As domestic manufacturers grapple with competitive pressures and supply chain complexities, their ability to manage escalating costs while fulfilling increased demand has pushed down profit. Industry revenue has been increasing at a CAGR of 2.2% over the past five years to total an estimated $7.9 billion in 2025, including an estimated 1.6% increase in 2025. Since 2020, smart meter manufacturers have faced hurdles. Labor shortages have been a persistent issue, with companies forced to raise wages to attract talent, as highlighted by the Census Bureau's report that over 20.0% of manufacturers couldn't reach full production capacity in Q3 2024. This, coupled with rising semiconductor and other input prices, has squeezed profit. The competitive landscape, intensified by both domestic producers and imports, has kept prices under pressure, forcing some manufacturers to sacrifice profit to boost sales. Looking ahead, the smart meter market is poised for both opportunities and challenges. While the US is nearing market saturation, with adoption rates expected to hit 94.0% by 2029, manufacturers will increasingly rely on new construction projects to drive sales. Growth in residential and commercial construction, alongside surging data center developments driven by AI, is expected to bolster demand. Growing energy needs on the heels of data center construction will also boost the sale of smart meters. However, while tariffs may push down import penetration, tariffs on foreign components could hike costs, potentially impacting profitability and the competitiveness of US products in international markets. Retaliatory tariffs by the Canadian government and high penetration rates in Canada may weaken exports to the country. Industry revenue is forecast to climb at a CAGR of 2.2% to total an estimated $8.8 billion through the end of 2030.

Global Smart Meter market size is expected to reach $40.42 billion by 2029 at 9.0%, segmented as by smart electric meter, advanced metering infrastructure (ami) electric meters, prepaid electric meters, two-way communication electric meters

Smart Meter Market is projected to reach USD 76.96 billion by 2032, growing at a CAGR of 14.08% from 2024-2032.

Smart meter market to reach $72.46B by 2035, growing at 8.84% CAGR. Explore smart grid and energy management trends.

Smart Electricity Meters Market Outlook

The global smart electricity meters market has experienced significant growth, with a market size of approximately $12 billion in 2023, and it is projected to reach a market size of $20 billion by 2032, expanding at a compound annual growth rate (CAGR) of 6.5%. This growth can be attributed to the increasing demand for efficient energy management, rising adoption of smart grid technologies, and government initiatives aimed at reducing energy consumption and promoting sustainable energy solutions. The integration of renewable energy sources into the grid and the need for real-time energy monitoring and control are driving the adoption of smart electricity meters across various sectors.

One of the primary growth factors for the smart electricity meters market is the global shift towards smart grid technology. Smart grids are being increasingly recognized as essential for modernizing electricity networks to enhance efficiency and reliability. Smart electricity meters form a crucial component of these grids, facilitating real-time data collection and communication between consumers and utility providers. This enhances the accuracy of billing, aids in demand response initiatives, and enables better load management. The urge to reduce energy wastage and optimize resource utilization aligns with the broader environmental goals of reducing carbon footprints and promoting sustainability, further propelling market growth.

Government policies and regulatory frameworks also play a significant role in boosting the smart electricity meters market. Many governments worldwide are implementing mandates or incentives to encourage the installation of smart meters. For instance, the European Union has laid down stringent targets for smart meter rollouts, aiming for near-universal coverage in member states. Similarly, countries in North America and Asia Pacific have initiated large-scale smart metering projects to enhance energy efficiency and grid reliability. These initiatives not only push the demand for smart meters but also foster technological advancements and innovations in the sector.

The rising awareness and consumer preference for efficient energy usage are also compelling factors for the market. Consumers are becoming increasingly conscious of their energy consumption patterns, driven by both cost-saving motives and environmental concerns. Smart electricity meters provide users with detailed insights into their energy usage, allowing them to make informed decisions about energy consumption and thereby reduce unnecessary costs. The ability to remotely control and monitor energy usage through advanced software applications enhances user convenience and satisfaction, further accelerating the adoption of smart meters in residential as well as commercial settings.

Regionally, the market for smart electricity meters is witnessing diversified growth patterns. North America and Europe are leading in terms of market penetration due to early adoption and significant investments in smart grid infrastructure. However, the Asia Pacific region is emerging rapidly as a lucrative market, driven by urbanization, industrialization, and supportive government policies in countries like China and India. Latin America and the Middle East & Africa are also progressively adopting smart metering solutions, albeit at a gradual pace due to economic and infrastructural constraints. The varying degrees of adoption and implementation across regions present both challenges and opportunities for market players.

Phase Analysis

The segmentation of the smart electricity meters market by phase into single phase and three phase meters is important as each caters to different end-user requirements. Single phase meters are predominantly used in residential settings where energy consumption is typically lower. They are easier to install and maintain, making them a cost-effective solution for homeowners. As more households become aware of the benefits of monitoring their energy usage, the demand for single phase meters is expected to rise. They offer basic functionalities required for residential energy management, such as reporting consumption patterns and providing real-time usage data, which can help in reducing electricity bills and enhancing energy saving measures.

On the other hand, three phase meters are primarily used in commercial and industrial applications where energy consumption is higher and more complex. These meters are designed to handle the large load and high energy consumption typical of these sectors. They offer advanced functionalities such as power

• 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.

According to Cognitive Market Research, the global Smart Meter market size was USD 23514.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 10.50% 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 9405.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 7054.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 5408.27 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1175.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.9% 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 470.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.2% from 2024 to 2031.
The less than 200 mL category is the fastest growing segment of the Smart Meter industry

Market Dynamics of Smart Meter Market

Key Drivers for Smart Meter Market

Increasing the Need for Utility System Monitoring to Boost Market Growth

The acceptance and deployment of smart meters, which are transforming the energy industry, are largely fueled by utility system monitoring. With their many advantages that improve operational efficiency and grid reliability, smart meters are being used more and more to monitor utility systems in real time. By facilitating easy communication with utility companies, these gadgets offer precise and current energy use statistics.Utility companies are better able to monitor the electrical grids in real time, identify outages early, and handle disturbances with efficiency, which enhances grid dependability and customer services. India is thought to be one of this market's most significant regions. Utility system monitoring, which offers real-time insights into patterns of energy consumption, is vital in counteracting the fast expansion of smart metering in India. Prepaid smart meters make up 476,422 of the 1.19 million smart meters deployed under the Integrated Power Development Scheme (IPDS) initiative, according to data that was made public.

Developing Trends in Regulation Activating Installations of Smart Meters

The market for smart meters is expanding due in large part to governments' increased legislative attempts to install smart power meters. With the help of national energy policies and financial incentives, these regulations are fostering a climate that is favorable for the broad use of smart meters, which will significantly expand the market and improve technology. These technical advancements support the goals of regulatory laws that encourage the installation of smart meters by improving the accuracy, efficiency, and transparency of energy consumption monitoring. For instance, the UK's Energy Act 2023 marked a turning point in the deployment of smart meters by establishing them as vital elements of the nation's energy transformation. This legislation, which went into effect in October, added new authority with the goal of making sure that the smart meter rollout is finished by 2028.

Restraint Factor for the Smart Meter Market

Infrastructure and Financial Difficulties with the Installation of Smart Meters will Limit Market Growth

Smart meters are much more expensive than standard meters, which frequently prevents their widespread adoption. For utilities in areas with tight resources, in particular, this poses a significant obstacle. Deploying and maintaining the required communication infrastructure comes at a significant cost in addition to the original outlay. Particularly in situations where financial resources are scarce or nonexistent, these costs may delay the adoption rate of smart meters. In addition, a strong communication network is necessary for the establishment of a smart grid, and this can be difficult in isolated or impoverished locations with inadequate infrastructure already in place. The deployment procedure may become even more complex as a result of these connectivity problems, which may prevent data from meters from reaching utilit...

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 |

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

| 1.0M | Mar 4 12:18 | 027ceb7b8fd77a4b11b3b497e9f0b174.parquet |

| 979K | Mar 4 12:18 | 03a4af696ff6a5c049736e9614f18b1b.parquet |

| 1.0M | Mar 4 12:18 | 03654abddf9a1b26f5fbbeea362a96ed.parquet |

| 1.0M | Mar 4 12:18 | 03acebcc4e7d39b6df5c72e01a3c35a6.parquet |

| 1.0M | Mar 4 12:18 | 039e60e1d03c2afd071085bdbd84bb69.parquet |

| 931K | Mar 4 12:18 | 036877a1563f01e6e830298c193071a6.parquet |

| 1.0M | Mar 4 12:18 | 02e45872f30f5a6a33972e8c3ba9c2e5.parquet |

| 662K | Mar 4 12:18 | 03a25f298431549a6bc0b1a58eca1f34.parquet |

| 635K | Mar 4 12:18 | 029a46275625a3cefc1f56b985067d15.parquet |

| 1.0M | Mar 4 12:18 | 0301309d6d1e06c60b4899061deb7abd.parquet |

| 1.0M | Mar 4 12:18 | 0291e323d7b1eb76bf680f6e800c2594.parquet |

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| 1.0M | Mar 4 12:18 | 02f670f059e1f834dfb8ba809c13a210.parquet |

| 987K | Mar 4 12:18 | 02af749aaf8feb59df7e78d5e5d550e0.parquet |

| 996K | Mar 4 12:18 | 0311d3c1d08ee0af3edda4dc260421d1.parquet |

| 1.0M | Mar 4 12:18 | 030a707019326e90b0ee3f35bde666e0.parquet |

| 955K | Mar 4 12:18 | 033441231b277b283191e0e1194d81e2.parquet |

| 995K | Mar 4 12:18 | 0317b0417d1ec91b5c243be854da8a86.parquet |

| 1.0M | Mar 4 12:18 | 02ef4e49b6fb50f62a043fb79118d980.parquet |

| 1.0M | Mar 4 12:18 | 0340ad82e9946be45b5401fc6a215bf3.parquet |

| 974K | Mar 4 12:18 | 03764b3b9a65886c3aacdbc85d952b19.parquet |

| 1.0M | Mar 4 12:18 | 039723cb9e421c5cbe5cff66d06cb4b6.parquet |

| 1.0M | Mar 4 12:18 | 0282f16ed6ef0035dc2313b853ff3f68.parquet |

| 1.0M | Mar 4 12:18 | 032495d70369c6e64ab0c4086583bee2.parquet |

| 900K | Mar 4 12:18 | 02c56641571fc9bc37448ce707c80d3d.parquet |

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

| 1.0M | Mar 4 12:18 | 02af272adccf45b6cdd4a7050c979f9f.parquet |

| 927K | Mar 4 12:18 | 02fc9a3b2b0871d3b6a1e4f8fe415186.parquet |

| 1.0M | Mar 4 12:18 | 03872674e2a78371ce4dfa5921561a8c.parquet |

| 881K | Mar 4 12:18 | 0344a09d90dbfa77481c5140bb376992.parquet |

| 1.0M | Mar 4 12:18 | 0351503e2b529f53bdae15c7fbd56fc0.parquet |

| 1.0M | Mar 4 12:18 | 033fe9c3a9ca39001af68366da98257c.parquet |

| 1.0M | Mar 4 12:18 | 02e70a1c64bd2da7eb0d62be870ae0d6.parquet |

| 1.0M | Mar 4 12:18 | 0296385692c9de5d2320326eaa000453.parquet |

| 962K | Mar 4 12:18 | 035254738f1cc8a31075d9fbe3ec2132.parquet |

| 991K | Mar 4 12:18 | 02e78f0d6a8fb96050053e188bf0f07c.parquet |

| 1.0M | Mar 4 12:18 | 039e4f37ed301110f506f551482d0337.parquet |

| 961K | Mar 4 12:18 | 039e2581430703b39c359dc62924a4eb.parquet |

| 999K | Mar 4 12:18 | 02c6f7e4b559a25d05b595cbb5626270.parquet |

| 1.0M | Mar 4 12:18 | 02dd91468360700a5b9514b109afb504.parquet |

| 938K | Mar 4 12:18 | 02e99c6bb9d3ca833adec796a232bac0.parquet |

| 589K | Mar 4 12:18 | 03aef63e26a0bdbce4a45d7cf6f0c6f8.parquet |

| 1.0M | Mar 4 12:18 | 02d1ca48a66a57b8625754d6a31f53c7.parquet |

| 1.0M | Mar 4 12:18 | 03af9ebf0457e1d451b83fa123f20a12.parquet |

| 1.0M | Mar 4 12:18 | 0289efb0e712486f00f52078d6c64a5b.parquet |

| 1.0M | Mar 4 12:18 | 03466ed913455c281ffeeaa80abdfff6.parquet |

| 1.0M | Mar 4 12:18 | 032d6f4b34da58dba02afdf5dab3e016.parquet |

| 1.0M | Mar 4 12:18 | 03406854f35a4181f4b0778bb5fc010c.parquet |

| 1.0M | Mar 4 12:18 | 0345fc286238bcea5b2b9849738c53a2.parquet |

| 1.0M | Mar 4 12:18 | 029ff5169155b57140821a920ad67c7e.parquet |

| 985K | Mar 4 12:18 | 02e4c9f3518f079ec4e5133acccb2635.parquet |

| 1.0M | Mar 4 12:18 | 03917c4f2aef487dc20238777ac5fdae.parquet |

| 969K | Mar 4 12:18 | 03aae0ab38cebcb160e389b2138f50da.parquet |

| 914K | Mar 4 12:18 |

In 2019, California was the U.S. state with the highest smart meter installations, with over ** million units. Texas followed, also above the ** million smart meters mark. Meanwhile, during this year, Pennsylvania had about *** million smart meters installed.