Correlated Time Series Forecasting

This dataset contains the traffic data in San Bernardino from July to August in 2016, with 170 detectors on 8 roads with a time interval of 5 minutes. This dataset is popular as a benchmark traffic forecasting dataset.

PeMS04 is a traffic forecasting benchmark.

METR-LA is a dataset for traffic prediction.

PEMS-BAY is a dataset for traffic prediction.

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. | Data Set Characteristics | Number of Instances | Area | Attribute Characteristics | Number of Attributes | Date Donated | Associated Tasks | Missing Values | | ------------------------- | ------------------- | -------- | ------------------------- | -------------------- | ------------ | ---------------------- | -------------- | | Multivariate, Time-Series | 2075259 | Physical | Real | 9 | 2012-08-30 | Regression, Clustering | Yes | ### Source: Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France ### Data Set Information: This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months). Notes: 1. (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. 2. 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. ### Attribute Information: 1. date: Date in format dd/mm/yyyy 2. time: time in format hh:mm:ss 3. global_active_power: household global minute-averaged active power (in kilowatt) 4. global_reactive_power: household global minute-averaged reactive power (in kilowatt) 5. voltage: minute-averaged voltage (in volt) 6. global_intensity: household global minute-averaged current intensity (in ampere) 7. 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). 8. 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. 9. 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. ### Relevant Papers: N/A ### Citation Request: This dataset is made available under the “Creative Commons Attribution 4.0 International (CC BY 4.0)” license

PeMSD7 is traffic data in District 7 of California consisting of the traffic speed of 228 sensors while the period is from May to June in 2012 (only weekdays) with a time interval of 5 minutes. This dataset is popular for benchmark the traffic forecasting models.

PeMS07 is a traffic forecasting benchmark.

Solar Power Data for Integration Studies NREL's Solar Power Data for Integration Studies are synthetic solar photovoltaic (PV) power plant data points for the United States representing the year 2006. The data are intended for use by energy professionals—such as transmission planners, utility planners, project developers, and university researchers—who perform solar integration studies and need to estimate power production from hypothetical solar plants. Data Methodologies The Solar Power Data for Integration Studies consist of 1 year (2006) of 5-minute solar power and hourly day-ahead forecasts for approximately 6,000 simulated PV plants. Solar power plant locations were determined based on the capacity expansion plan for high-penetration renewables in Phase 2 of the Western Wind and Solar Integration Study and the Eastern Renewable Generation Integration Study. NREL generated the 5-minute data set using the Sub-Hour Irradiance Algorithm. The day-ahead solar forecast data for locations in the western United States were generated by 3TIER based on numerical weather predication simulations for Phase 1 of the Western Wind and Solar Integration Study. NREL generated the day-ahead solar forecast data in eastern U.S. locations using the Weather Research and Forecasting model. The data are for specific years and should not be assumed to be representative of typical radiation levels for a site. These data should not generally be used for site-specific project development work. Naming Convention The naming convention of the state-wise solar power data (.csv files) from the Solar Integration Studies is as follows. Data Type_Latitude_Longitude_Weather Year_PV Type_CapacityMW_Time Interval _Min.csv Data Type Actual: Real power output DA: Day ahead forecast HA4: 4 hour ahead forecast Weather Year: The PV data is based on the particular year's known weather condition. PV Type UPV: Utility scale PV DPV: Distributed PV Note: The practical difference between UPV and DPV is in the configurations (UPV has single axis tracking while DPV is fixed tilt equaling to latitude) and the smoothing (both are run through a low-pass filter, the DPV will have more of the high frequency variability smoothed out). Capacity: Installed capacity in MW Time Interval: PV generation data reading interval in minutes. Contact Yingchen Zhang Manager, Sensing, Measurement, and Forecasting Group Yingchen.Zhang@nrel.gov 303-384-7090