{"url":"/dataset/electricity","name":"Electricity","full_name":"Individual household electric power consumption Data Set","description_markdown":"**Abstract**: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.\r\n\r\n| Data Set Characteristics  | Number of Instances | Area     | Attribute Characteristics | Number of Attributes | Date Donated | Associated Tasks       | Missing Values |\r\n| ------------------------- | ------------------- | -------- | ------------------------- | -------------------- | ------------ | ---------------------- | -------------- |\r\n| Multivariate, Time-Series | 2075259             | Physical | Real                      | 9                    | 2012-08-30   | Regression, Clustering | Yes            |\r\n\r\n### Source:\r\n\r\nGeorges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France\r\nAlice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France\r\n\r\n### Data Set Information:\r\n\r\nThis archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months).\r\nNotes:\r\n\r\n1. (global_active_power\\*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3.\r\n2. The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.\r\n\r\n### Attribute Information:\r\n\r\n1. `date`: Date in format `dd/mm/yyyy`\r\n2. `time`: time in format `hh:mm:ss`\r\n3. `global_active_power`: household global minute-averaged active power (in kilowatt)\r\n4. `global_reactive_power`: household global minute-averaged reactive power (in kilowatt)\r\n5. `voltage`: minute-averaged voltage (in volt)\r\n6. `global_intensity`: household global minute-averaged current intensity (in ampere)\r\n7. `sub_metering_1`: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).\r\n8. `sub_metering_2`: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.\r\n9. `sub_metering_3`: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.\r\n\r\n### Relevant Papers:\r\n\r\nN/A\r\n\r\n### Citation Request:\r\n\r\nThis dataset is made available under the “Creative Commons Attribution 4.0 International (CC BY 4.0)” license","description_withheld":null,"homepage":"https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption","introduced_date":"2021-12-13","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://archive.ics.uci.edu/ml/citation_policy.html"},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series Analysis","url":"/task/time-series","datasets_with_task":"/datasets/task/time-series"},{"name":"Multivariate Time Series Forecasting","url":"/task/multivariate-time-series-forecasting","datasets_with_task":"/datasets/task/multivariate-time-series-forecasting"},{"name":"GLinear","url":"/task/glinear","datasets_with_task":"/datasets/task/glinear"},{"name":"Core set discovery","url":"/task/core-set-discovery","datasets_with_task":"/datasets/task/core-set-discovery"},{"name":"Multivariate Time Series Imputation","url":"/task/multivariate-time-series-imputation","datasets_with_task":"/datasets/task/multivariate-time-series-imputation"},{"name":"Correlated Time Series Forecasting","url":"/task/correlated-time-series-forecasting","datasets_with_task":"/datasets/task/correlated-time-series-forecasting"},{"name":"Univariate Time Series Forecasting","url":"/task/univariate-time-series-forecasting","datasets_with_task":"/datasets/task/univariate-time-series-forecasting"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Electricity"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/electricity-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/WenjieDu/TSDB","url":"https://github.com/WenjieDu/TSDB","frameworks":[]}],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/univariate-time-series-forecasting-on","task":"Univariate Time Series Forecasting","dataset_variant":"Electricity","rows":12,"metrics":["RRSE"],"first_row_in_archive_order":{"model":"MTGNN (3 step)","paper":"/paper/connecting-the-dots-multivariate-time-series","metrics":{"RRSE":"0.0745"},"code_links":[{"title":"benedekrozemberczki/pytorch_geometric_temporal","url":"https://github.com/benedekrozemberczki/pytorch_geometric_temporal"},{"title":"nnzhan/MTGNN","url":"https://github.com/nnzhan/MTGNN"},{"title":"pwc-1/Paper-9","url":"https://github.com/pwc-1/Paper-9/tree/main/6/Cybertron"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/core-set-discovery-on-electricity","task":"Core set discovery","dataset_variant":"Electricity","rows":1,"metrics":["F1(10-fold)"],"first_row_in_archive_order":{"model":"EvoCore","paper":"/paper/uncovering-coresets-for-classification-with","metrics":{"F1(10-fold)":"69.3"},"code_links":[{"title":"pietrobarbiero/meco","url":"https://github.com/pietrobarbiero/meco"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/correlated-time-series-forecasting-on","task":"Correlated Time Series Forecasting","dataset_variant":"Electricity","rows":1,"metrics":["FLOPs(M)","Parameters(K)"],"first_row_in_archive_order":{"model":"LightCTS","paper":"/paper/lightcts-a-lightweight-framework-for","metrics":{"FLOPs(M)":"239","Parameters(K)":"27"},"code_links":[{"title":"ai4cts/lightcts","url":"https://github.com/ai4cts/lightcts"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/glinear-on-electricity","task":"GLinear","dataset_variant":"Electricity","rows":1,"metrics":["MSE "],"first_row_in_archive_order":{"model":"GLinear","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE ":"0.0883"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-forecasting-on-44","task":"Multivariate Time Series Forecasting","dataset_variant":"Electricity","rows":1,"metrics":["MSE "],"first_row_in_archive_order":{"model":"GLinear","paper":"/paper/bridging-simplicity-and-sophistication-using-1","metrics":{"MSE ":"0.0883"},"code_links":[{"title":"t-rizvi/GLinear","url":"https://github.com/t-rizvi/GLinear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multivariate-time-series-imputation-on-3","task":"Multivariate Time Series Imputation","dataset_variant":"Electricity","rows":1,"metrics":["MAE (100 steps, 10% data missing)"],"first_row_in_archive_order":{"model":"SAITS","paper":"/paper/saits-self-attention-based-imputation-for","metrics":{"MAE (100 steps, 10% data missing)":"0.735"},"code_links":[{"title":"WenjieDu/PyPOTS","url":"https://github.com/WenjieDu/PyPOTS"},{"title":"WenjieDu/SAITS","url":"https://github.com/WenjieDu/SAITS"},{"title":"gorgen2020/LSSDM_imputation","url":"https://github.com/gorgen2020/LSSDM_imputation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/bridging-simplicity-and-sophistication-using-1","title":"Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series Prediction","date":"2025-01-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/lightcts-a-lightweight-framework-for","title":"LightCTS: A Lightweight Framework for Correlated Time Series Forecasting","date":"2023-02-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/saits-self-attention-based-imputation-for","title":"SAITS: Self-Attention-based Imputation for Time Series","date":"2022-02-17","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/connecting-the-dots-multivariate-time-series","title":"Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks","date":"2020-05-24","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/uncovering-coresets-for-classification-with","title":"Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms","date":"2020-02-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/temporal-pattern-attention-for-multivariate","title":"Temporal Pattern Attention for Multivariate Time Series Forecasting","date":"2018-09-12","rows_on_this_dataset":4,"code_links":4,"syntology":null},{"paper":"/paper/modeling-long-and-short-term-temporal","title":"Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks","date":"2017-03-21","rows_on_this_dataset":4,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":19,"samples_ran":4,"samples_unverified":15,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}