#Data and Problem Description

More accurate forecasts of building energy consumption mean better planning and more efficient energy use. So The objective is to forecast energy consumption from following data: (For each data set, several test periods over which a forecast is required will be specified.)

Historical Consumption

A selected time series of consumption data for over 260 buildings.

-obs_id - An arbitrary ID for the observationaa -SiteId - An arbitrary ID number for the building, matches across datasets -ForecastId - An ID for a timeseries that is part of a forecast (can be matched with the submission file) -Timestamp - The time of the measurement -Value - A measure of consumption for that building

Building Metadata

Additional information about the included buildings.

-SiteId - An arbitrary ID number for the building, matches across datasets -Surface - The surface area of the building -Sampling - The number of minutes between each observation for this site. The timestep size for each ForecastId can be found in the separate "Submission Forecast Period" file on the data download page. -BaseTemperature - The base temperature for the building -IsDayOff - True if DAY_OF_WEEK is not a work day

Historical Weather Data

This dataset contains temperature data from several stations near each site. For each site several temperature measurements were retrieved from stations in a radius of 30 km if available. Note: Not all sites will have available weather data.

Note: Weather data is available for test periods under the assumption that reasonably accurate forecasts will be available to algorithms that the time that we are attempting to make predictions about the future.

-SiteId - An arbitrary ID number for the building, matches across datasets -Timestamp - The time of the measurement -Temperature - The temperature as measured at the weather station -Distance - The distance in km from the weather station to the building in km

Public Holidays

Public holidays at the sites included in the dataset, which may be helpful for identifying days where consumption may be lower than expected.Note: Not all sites will have available public holiday data.

-SiteId - An arbitrary ID number for the building, matches across datasets -Date - The date of the holiday -Holiday - The name of the holiday

Acknowledgements

Forecasting energy consumption data published by Schneider Electric.

Inspiration

Three time horizons and time steps are distinguished for more than 260 building sites are provided. The goal is either:

• To forecast the consumption for each quarter for the length of time specified by the submission format.
• To forecast the consumption for each hour for the length of time specified by the submission format.
• To forecast the consumption for each day for the length of time specified by the submission format.

Historical data are given at the granularity that is required for the consumption forecast. So, when historical data are given by steps of 15 minutes, forecasts are required by steps of 15 minutes. When historical data are given by steps of 1 hour, forecasts are required by steps of 1 hour. When historical data are given by steps of 1 day, forecasts are required by steps of 1 day.

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