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11 results for “smart meter data”
GoiEner smart meters data
<ul> <li><strong>Name</strong>: GoiEner smart meters data</li> <li><strong>Summary</strong>: 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: <ul> <li><strong>raw.tzst</strong>, contains raw time series of all GoiEner clients (any date, any length, may have missing samples).</li> <li><strong>imp-pre.tzst</strong>, contains processed time series (imputation of missing samples), longer than one year, collected before March 1, 2020.</li> <li><strong>imp-in.tzst</strong>, contains processed time series (imputation of missing samples), longer than one year, collected between March 1, 2020 and May 30, 2021.</li> <li><strong>imp-post.tzst</strong>, contains processed time series (imputation of missing samples), longer than one year, collected after May 30, 2020.</li> <li><strong>metadata.csv</strong>, contains relevant information for each time series.</li> </ul> </li> <li><strong>License</strong>: CC-BY-SA</li> <li><strong>Acknowledge</strong>: 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.</li> <li><strong>Disclaimer</strong>: 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.</li> <li><strong>Collection Date</strong>: From November 2, 2014 to June 8, 2022.</li> <li><strong>Publication Date</strong>: December 1, 2022.</li> <li><strong>DOI</strong>: 10.5281/zenodo.7362094</li> <li><strong>Other repositories</strong>: None.</li> <li><strong>Author</strong>: GoiEner, University of Deusto.</li> <li><strong>Objective of collection</strong>: This dataset was originally used to establish a methodology for clustering households according to their electricity consumption.</li> <li><strong>Description</strong>: The meaning of each column is described next for each file. <ul> <li><strong>raw.tzst</strong>: (no column names provided) <ul> <li>timestamp;</li> <li>electricity consumption in kWh.</li> </ul> </li> <li><strong>imp-pre.tzst</strong>, <strong>imp-in.tzst</strong>, <strong>imp-post.tzst</strong>: <ul> <li>“<em>timestamp</em>”: timestamp;</li> <li>“<em>kWh</em>”: electricity consumption in kWh;</li> <li>“<em>imputed</em>”: binary value indicating whether the row has been obtained by imputation.</li> </ul> </li> <li><strong>metadata.csv</strong>: <ul> <li>“<em>user</em>”: 64-character identifying a user;</li> <li>“<em>start_date</em>”: initial timestamp of the time series;</li> <li>“<em>end_date</em>”: final timestamp of the time series;</li> <li>“<em>length_days</em>”: number of days elapsed between the initial and the final timestamps;</li> <li>“<em>length_years</em>”: number of years elapsed between the initial and the final timestamps;</li> <li>“<em>potential_samples</em>”: number of samples that should be between the initial and the final timestamps of the time series if there were no missing values;</li> <li>“<em>actual_samples</em>”: number of actual samples of the time series;</li> <li>“<em>missing_samples_abs</em>”: number of potential samples minus actual samples;</li> <li>“<em>missing_samples_pct</em>”: potential samples minus actual samples as a percentage;</li> <li>“<em>contract_start_date</em>”: contract start date; “<em>contract_end_date</em>”: contract end date;</li> <li>“<em>contracted_tariff</em>”: type of tariff contracted (2.X: households and SMEs, 3.X: SMEs with high consumption, 6.X: industries, large commercial areas, and farms);</li> <li>“<em>self_consumption_type</em>”: the type of self-consumption to which the users are subscribed;</li> <li>“<em>p1</em>”, “<em>p2</em>”, “<em>p3</em>”, “<em>p4</em>”, “<em>p5</em>”, “<em>p6</em>”: contracted power (in kW) for each of the six time slots;</li> <li>“<em>province</em>”: province where the user is located;</li> <li>“<em>municipality</em>”: municipality where the user is located (municipalities below 50.000 inhabitants have been removed);</li> <li>“<em>zip_code</em>”: post code (post codes of municipalities below 50.000 inhabitants have been removed);</li> <li>“<em>cnae</em>”: CNAE (<em>Clasificación Nacional de Actividades Económicas</em>) code for economic activity classification.</li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps</strong>: 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).</li> <li><strong>Reuse:</strong> This dataset is related to datasets: <ul> <li>"A database of features extracted from different electricity load profiles datasets" (DOI 10.5281/zenodo.7382818), where time series feature extraction has been performed. </li> <li>"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.</li> </ul> </li> <li><strong>Update policy:</strong> There might be a single update in mid-2023.</li> <li><strong>Ethics and legal aspects:</strong> 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.</li> <li><strong>Technical aspects:</strong> <ul> <li><strong>raw.tzst</strong> contains a 15.1 GB folder with 25,559 CSV files;</li> <li><strong>imp-pre.tzst </strong>contains a 6.28 GB folder with 12,149 CSV files;</li> <li><strong>imp-in.tzst</strong> contains a 4.36 GB folder with 15.562 CSV files; and</li> <li><strong>imp-post.tzst</strong> contains a 4.01 GB folder with 17.519 CSV files.</li> </ul> </li> <li><strong>Other:</strong> None.</li> </ul>
Swiss Smart Meter Data - CKW 2021/2022 - anonymized individual metering points
<p>Cleaned Swiss smart meter data based on a collection from CKW AG (see <a href="http://opendata.ckw.ch">opendata.ckw.ch</a>)</p> <ul> <li>Duration: 2 years (1. Jan 2021 - 31. Dec 2022, CET timestamp)</li> <li>Location: Canton Lucerne, Switzerland</li> <li>Interval: 15 minutes</li> <li>Values: Active Energy (kWh) </li> <li>Meters in each year: 4959 (see filtered_IDs.csv for all IDs)</li> </ul> <p>The original dataset has been filtered based</p> <ul> <li>on missing data </li> </ul> <p>This means, all 4959 meters have consumption and reported values over the full duration of 2 years.<br> Files are available as space-saving parquet files per day in year. Number in filename is number of day within the year.<br> <br> 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</p>
A 2.5-year campus-level smart meter database with equipment data for energy analytics
Open the record for dataset details and reuse information.
REFLOW - Cluj Napoca Pilot Smart Meter Data
<p>Smart Metering Data collected by the Pilot in Cluj Napoca within the context of the Reflow-EU-project.</p>
Smart Meter Water Consumption Measurements – Additional Evaluation Data
<p><strong>Data</strong><br> We collected the cumulative water consumption data in Germany using the commercially available smart meters <em>Hydrus 1.3, DN 20,00</em> from <em>Diehl Metering</em>. For evaluation purpose we further collected some label data on activity events and on out-of-house periods: <br> <em>Activity Data:</em> During defined time spans, we performed two typical household activities associated with water consumption and recorded the time at which they were performed. Specifically, we turned on various taps for 30 seconds and flushed the toilets.<br> We avoided concurrent water consumption and always waited at least 5~minutes between two activities.<br> <em>Out-of-house Data</em>: A separate data set is also collected in two test households. Besides the readings from the smart water meters, the absence times of the residents were also collected using the diary method. We handed the residents a log form in which they recorded those time points when no residents were in the house. The residents were also asked to note whether a washing machine or a dishwasher was running during a period of absence.</p> <p>Further information on data collection and description of the dataset can be found in Section 3 and Section 6 of the original article, which is available at the following DOI: <strong>TBD.</strong></p> <p><strong>Structure</strong><br> The <em>Zenodo</em> Archive has two folders, which distinguishes the two different sets of evaluation data. <br> The <em>Activity Data</em> contains subfolders for each of the examined frame. Each of these subfolders contains general information about the frame (e.g., household ID), the smart meter readings (<em>smartmeter.csv</em>) and a list of performed activities (<em>activities.csv</em>).<br> The <em>Out-of-house Data</em> contains two subfolders for the sampled households. These subfolders in turn contain a <em>periods.csv</em> file with the recorded times of presence, a <em>smartmeter.csv </em>file with the smart meter readings during the survey, and a general <em>info</em> document.</p>
GoiEner smart meters raw data
<blockquote> <p>For processed data files more suitable for use, please refer to the <strong><em>GoiEner Smart Meters Data</em></strong> dataset: <a href="https://zenodo.org/record/7362094">DOI 10.5281/zenodo.7362094</a></p> </blockquote> <p> </p> <p>The <em><strong>GoiEner Raw Files</strong></em> dataset contains a complete set of raw data from the customer database of the Spanish renewable energy cooperative GoiEner, obtained from smart meters. Founded in the Basque Country in 2012, GoiEner has made available this extensive dataset, which includes the <strong>raw electricity consumption data</strong> (and self-generation) for all its customers. The supply points provided comprise a diverse range of customers, such as households, offices, small and medium-sized enterprises (SMEs), industrial buildings, and public facilities.<br> <br> This dataset spans the entire period from the first records using smart meters in <strong>late 2014 until June 2022</strong>. It consists of <strong>71,048 files</strong> containing information on consumption, generation, contracted power, pricing, and other related data for each supply point in the grid. These files serve as the primary source for managing electricity supply contracts between distributors and retailers, as well as for user billing. The formats and specifications of the files adhere to the guidelines set by the Spanish electricity market regulator, the <em>National Commission of Markets and Competition</em> (<em>CNMC</em>), which establishes the <em>Electricity Metering Information System</em> (<em>SIMEL</em>).</p>
Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings
<p>This dataset includes three years of cleaned hourly data from 3021 commercial smart heat meters installed in Danish residential buildings. The data are screened, interpolated to be equidistant, and missing values were imputed using a weighted moving average combined with a scaling algorithm to obey the data's cumulative trend. The original (anonymised) raw data of 3127 smart heat meters are also provided to increase transparency and reproducibility. Together with the consumption data, contextual information about the construction year, the type of building, and, if available, the energy label for the smart heat meters buildings (for all 3127 buildings of the raw data) are provided. The unique meter ID can link this data to the consumption data. A .pdf document describing the purpose of every data column is given in the folder '01_Data'. </p> <p>Besides this, three figures visualising the z-normalised data in different temporal resolutions are provided. Next to the data and the data visualisation, all code, written in R, used for data processing and extensive technical validation of the data is included.</p> <p>For a more extensive description, we would like to refer to our peer-reviewed open-access article describing the dataset and its processing: <a href="https://doi.org/10.1038/s41597-022-01502-3">https://doi.org/10.1038/s41597-022-01502-3</a>. Please also consider citing this article if you use this dataset. (Schaffer, M., Tvedebrink, T. & Marszal-Pomianowska, A. Three years of hourly data from 3021 smart heat meters installed in Danish residential buildings. Sci Data 9,<strong> </strong>420 (2022). https://doi.org/10.1038/s41597-022-01502-3)</p>
CKW Smart Meter Data
<h1>Overview</h1> <p>The <a href="https://www.ckw.ch/ueber-ckw">CKW Group</a> is a distribution system operator that supplies more than 200,000 end customers in Central Switzerland. Since <a href="https://www.ckw.ch/ueber-ckw/medienstelle/medienmitteilungen/2022/ckw-veroeffentlicht-detaillierte-daten-zum-stromverbrauch-im-kanton-luzern">October 2022</a>, CKW publishes anonymised and aggregated data from smart meters that measure electricity consumption in canton Lucerne. This unique dataset is accessible in the <a href="https://www.ckw.ch/landingpages/open-data">ckw.ch/opendata</a> platform.</p> <ul> <li>Data set <a href="https://open.data.axpo.com/%24web/index.html#dataset-a">A</a> - anonimised smart meter data</li> <li>Data set B - aggregated smart meter data</li> </ul> <h1>Contents of this data set</h1> <p>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 <strong>id</strong> field of the corresponding smart meter anonymised data. Example: <em>027ceb7b8fd77a4b11b3b497e9f0b174.parquet</em></p> <p>The orginal CKW data is available for download at <a href="https://open.data.axpo.com/%24web/index.html#dataset-a">https://open.data.axpo.com/%24web/index.html#dataset-a</a> as a (gzip-compressed) csv files, which are are split into one file per calendar month. The columns in the files csv are:</p> <ul> <li>id: the anonymized counter ID (text)</li> <li>timestamp: the UTC time at the beginning of a 15-minute time window to which the consumption refers (ISO-8601 timestamp)</li> <li>value_kwh: the consumption in kWh in the time window under consideration (float)</li> </ul> <p>In this archive, data from: </p> <blockquote> <p>| Dateigrösse | Export Datum | Zeitraum | Dateiname |<br>| ----------- | ------------ | -------- | --------- |<br>| 4.2GiB | 2024-04-20 | 202402 | ckw_opendata_smartmeter_dataset_a_202402.csv.gz |<br>| 4.5GiB | 2024-03-21 | 202401 | ckw_opendata_smartmeter_dataset_a_202401.csv.gz |<br>| 4.5GiB | 2024-02-20 | 202312 | ckw_opendata_smartmeter_dataset_a_202312.csv.gz |<br>| 4.4GiB | 2024-01-20 | 202311 | ckw_opendata_smartmeter_dataset_a_202311.csv.gz |<br>| 4.5GiB | 2023-12-20 | 202310 | ckw_opendata_smartmeter_dataset_a_202310.csv.gz |<br>| 4.4GiB | 2023-11-20 | 202309 | ckw_opendata_smartmeter_dataset_a_202309.csv.gz |<br>| 4.5GiB | 2023-10-20 | 202308 | ckw_opendata_smartmeter_dataset_a_202308.csv.gz |<br>| 4.6GiB | 2023-09-20 | 202307 | ckw_opendata_smartmeter_dataset_a_202307.csv.gz |<br>| 4.4GiB | 2023-08-20 | 202306 | ckw_opendata_smartmeter_dataset_a_202306.csv.gz |<br>| 4.6GiB | 2023-07-20 | 202305 | ckw_opendata_smartmeter_dataset_a_202305.csv.gz |<br>| 3.3GiB | 2023-06-20 | 202304 | ckw_opendata_smartmeter_dataset_a_202304.csv.gz |<br>| 4.6GiB | 2023-05-24 | 202303 | ckw_opendata_smartmeter_dataset_a_202303.csv.gz |<br>| 4.2GiB | 2023-04-20 | 202302 | ckw_opendata_smartmeter_dataset_a_202302.csv.gz |<br>| 4.7GiB | 2023-03-20 | 202301 | ckw_opendata_smartmeter_dataset_a_202301.csv.gz |<br>| 4.6GiB | 2023-03-15 | 202212 | ckw_opendata_smartmeter_dataset_a_202212.csv.gz |<br>| 4.3GiB | 2023-03-15 | 202211 | ckw_opendata_smartmeter_dataset_a_202211.csv.gz |<br>| 4.4GiB | 2023-03-15 | 202210 | ckw_opendata_smartmeter_dataset_a_202210.csv.gz |<br>| 4.3GiB | 2023-03-15 | 202209 | ckw_opendata_smartmeter_dataset_a_202209.csv.gz |<br>| 4.4GiB | 2023-03-15 | 202208 | ckw_opendata_smartmeter_dataset_a_202208.csv.gz |<br>| 4.4GiB | 2023-03-15 | 202207 | ckw_opendata_smartmeter_dataset_a_202207.csv.gz |<br>| 4.2GiB | 2023-03-15 | 202206 | ckw_opendata_smartmeter_dataset_a_202206.csv.gz |<br>| 4.3GiB | 2023-03-15 | 202205 | ckw_opendata_smartmeter_dataset_a_202205.csv.gz |<br>| 4.2GiB | 2023-03-15 | 202204 | ckw_opendata_smartmeter_dataset_a_202204.csv.gz |<br>| 4.1GiB | 2023-03-15 | 202203 | ckw_opendata_smartmeter_dataset_a_202203.csv.gz |<br>| 3.5GiB | 2023-03-15 | 202202 | ckw_opendata_smartmeter_dataset_a_202202.csv.gz |<br>| 3.7GiB | 2023-03-15 | 202201 | ckw_opendata_smartmeter_dataset_a_202201.csv.gz |<br>| 3.5GiB | 2023-03-15 | 202112 | ckw_opendata_smartmeter_dataset_a_202112.csv.gz |<br>| 3.1GiB | 2023-03-15 | 202111 | ckw_opendata_smartmeter_dataset_a_202111.csv.gz |<br>| 3.0GiB | 2023-03-15 | 202110 | ckw_opendata_smartmeter_dataset_a_202110.csv.gz |<br>| 2.7GiB | 2023-03-15 | 202109 | ckw_opendata_smartmeter_dataset_a_202109.csv.gz |<br>| 2.6GiB | 2023-03-15 | 202108 | ckw_opendata_smartmeter_dataset_a_202108.csv.gz |<br>| 2.4GiB | 2023-03-15 | 202107 | ckw_opendata_smartmeter_dataset_a_202107.csv.gz |<br>| 2.1GiB | 2023-03-15 | 202106 | ckw_opendata_smartmeter_dataset_a_202106.csv.gz |<br>| 2.0GiB | 2023-03-15 | 202105 | ckw_opendata_smartmeter_dataset_a_202105.csv.gz |<br>| 1.7GiB | 2023-03-15 | 202104 | ckw_opendata_smartmeter_dataset_a_202104.csv.gz |<br>| 1.6GiB | 2023-03-15 | 202103 | ckw_opendata_smartmeter_dataset_a_202103.csv.gz |<br>| 1.3GiB | 2023-03-15 | 202102 | ckw_opendata_smartmeter_dataset_a_202102.csv.gz |<br>| 1.3GiB | 2023-03-15 | 202101 | ckw_opendata_smartmeter_dataset_a_202101.csv.gz |</p> </blockquote> <p>was processed into partitioned parquet files, and then organised by <strong>id</strong> into parquet files with data from single smart meters. </p> <p>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.</p> <p>It consists of the following parquet files:</p> <blockquote> <div> <div>| Size | Date | Name |</div> <div>|------|------|------|</div> <div>| 1.0M | Mar 4 12:18 | 027ceb7b8fd77a4b11b3b497e9f0b174.parquet |</div> <div>| 979K | Mar 4 12:18 | 03a4af696ff6a5c049736e9614f18b1b.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03654abddf9a1b26f5fbbeea362a96ed.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03acebcc4e7d39b6df5c72e01a3c35a6.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 039e60e1d03c2afd071085bdbd84bb69.parquet |</div> <div>| 931K | Mar 4 12:18 | 036877a1563f01e6e830298c193071a6.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02e45872f30f5a6a33972e8c3ba9c2e5.parquet |</div> <div>| 662K | Mar 4 12:18 | 03a25f298431549a6bc0b1a58eca1f34.parquet |</div> <div>| 635K | Mar 4 12:18 | 029a46275625a3cefc1f56b985067d15.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0301309d6d1e06c60b4899061deb7abd.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0291e323d7b1eb76bf680f6e800c2594.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0298e58930c24010bbe2777c01b7644a.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0362c5f3685febf367ebea62fbc88590.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0390835d05372cb66f6cd4ca662399e8.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02f670f059e1f834dfb8ba809c13a210.parquet |</div> <div>| 987K | Mar 4 12:18 | 02af749aaf8feb59df7e78d5e5d550e0.parquet |</div> <div>| 996K | Mar 4 12:18 | 0311d3c1d08ee0af3edda4dc260421d1.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 030a707019326e90b0ee3f35bde666e0.parquet |</div> <div>| 955K | Mar 4 12:18 | 033441231b277b283191e0e1194d81e2.parquet |</div> <div>| 995K | Mar 4 12:18 | 0317b0417d1ec91b5c243be854da8a86.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02ef4e49b6fb50f62a043fb79118d980.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0340ad82e9946be45b5401fc6a215bf3.parquet |</div> <div>| 974K | Mar 4 12:18 | 03764b3b9a65886c3aacdbc85d952b19.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 039723cb9e421c5cbe5cff66d06cb4b6.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0282f16ed6ef0035dc2313b853ff3f68.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 032495d70369c6e64ab0c4086583bee2.parquet |</div> <div>| 900K | Mar 4 12:18 | 02c56641571fc9bc37448ce707c80d3d.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 027b7b950689c337d311094755697a8f.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02af272adccf45b6cdd4a7050c979f9f.parquet |</div> <div>| 927K | Mar 4 12:18 | 02fc9a3b2b0871d3b6a1e4f8fe415186.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03872674e2a78371ce4dfa5921561a8c.parquet |</div> <div>| 881K | Mar 4 12:18 | 0344a09d90dbfa77481c5140bb376992.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0351503e2b529f53bdae15c7fbd56fc0.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 033fe9c3a9ca39001af68366da98257c.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02e70a1c64bd2da7eb0d62be870ae0d6.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0296385692c9de5d2320326eaa000453.parquet |</div> <div>| 962K | Mar 4 12:18 | 035254738f1cc8a31075d9fbe3ec2132.parquet |</div> <div>| 991K | Mar 4 12:18 | 02e78f0d6a8fb96050053e188bf0f07c.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 039e4f37ed301110f506f551482d0337.parquet |</div> <div>| 961K | Mar 4 12:18 | 039e2581430703b39c359dc62924a4eb.parquet |</div> <div>| 999K | Mar 4 12:18 | 02c6f7e4b559a25d05b595cbb5626270.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02dd91468360700a5b9514b109afb504.parquet |</div> <div>| 938K | Mar 4 12:18 | 02e99c6bb9d3ca833adec796a232bac0.parquet |</div> <div>| 589K | Mar 4 12:18 | 03aef63e26a0bdbce4a45d7cf6f0c6f8.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02d1ca48a66a57b8625754d6a31f53c7.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03af9ebf0457e1d451b83fa123f20a12.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0289efb0e712486f00f52078d6c64a5b.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03466ed913455c281ffeeaa80abdfff6.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 032d6f4b34da58dba02afdf5dab3e016.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03406854f35a4181f4b0778bb5fc010c.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 0345fc286238bcea5b2b9849738c53a2.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 029ff5169155b57140821a920ad67c7e.parquet |</div> <div>| 985K | Mar 4 12:18 | 02e4c9f3518f079ec4e5133acccb2635.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 03917c4f2aef487dc20238777ac5fdae.parquet |</div> <div>| 969K | Mar 4 12:18 | 03aae0ab38cebcb160e389b2138f50da.parquet |</div> <div>| 914K | Mar 4 12:18 | 02bf87b07b64fb5be54f9385880b9dc1.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02776685a085c4b785a3885ef81d427a.parquet |</div> <div>| 947K | Mar 4 12:18 | 02f5a82af5a5ffac2fe7551bf4a0a1aa.parquet |</div> <div>| 992K | Mar 4 12:18 | 039670174dbc12e1ae217764c96bbeb3.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 037700bf3e272245329d9385bb458bac.parquet |</div> <div>| 602K | Mar 4 12:18 | 0388916cdb86b12507548b1366554e16.parquet |</div> <div>| 939K | Mar 4 12:18 | 02ccbadea8d2d897e0d4af9fb3ed9a8e.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02dc3f4fb7aec02ba689ad437d8bc459.parquet |</div> <div>| 1.0M | Mar 4 12:18 | 02cf12e01cd20d38f51b4223e53d3355.parquet |</div> <div>| 993K | Mar 4 12:18 | 0371f79d154c00f9e3e39c27bab2b426.parquet |</div> </div> </blockquote> <div> <div> <p>where each file contains data from a single smart meter. </p> <h1>Acknowledgement</h1> <p>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. <a href="https://www.eranet-smartenergysystems.eu/Calls/EnerDigit_Calls_funding/Joint_Call_2020">ERA-Net Smart Energy Systems joint call</a> on digital transformation for green energy transition.</p> </div> </div>
LADPU Smart Meter Data
<p>This dataset contains the electric power consumption data from the Los Alamos Public Utility Department (LADPU) in New Mexico, USA. The data was collected by Landis+Gyr smart meters devices on 1,757 households at North Mesa, Los Alamos, NM. The sampling rate is one observation every fifteen minutes (i.e., 96 observations per day). For most customers, the data spans about six years, from July 30, 2013 to December 30, 2019. However, for some customers, the period is reduced. The dataset contains missing values and duplicated measurements. </p>
LADPU Smart Meter Data
Open the record for dataset details and reuse information.
Smart meters data involved in Spanish Pilot
<p>File containing the smart meters data involved in the project to forecasting system, distribution management system and optimiz<acion to interacta with Invade platform</p>
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