Power Laws: Forecasting Energy Consumption

More accurate forecasts of building energy consumption mean better planning and more efficient energy use. In this challenge, competitors used machine learning to build the most accurate predictions of the future from limited data in the past. #energy

€23,000 in prizes
mar 2018
1,032 joined

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This is one of three challenges in the “Power Laws” series being run simultaneoulsy by Schneider Electric. Each challenge explores a different aspect of energy efficiency and smart energy management. The winning algorithms from these competitions will be released under an open source license in order to spread understanding about how energy modeling works and what approaches are most effective.

Power Laws: Forecasting Energy Consumption

Building energy forecasting has gained momentum with the increase of building energy efficiency research and solution development. Indeed, forecasting the global energy consumption of a building can play a pivotal role in the operations of the building. It provides an initial check for facility managers and building automation systems to mark any discrepancy between expected and actual energy use. Accurate energy consumption forecasts are also used by facility managers, utility companies and building commissioning projects to implement energy-saving policies and optimize the operations of chillers, boilers and energy storage systems.

Planning and forecasting the use of the electrical energy is the backbone of effective operations. Energy demand forecasting is used within multiple Schneider Electric offers, and different methods require more or less data. Schneider Electric is interested in more precise and robust forecasting methods that do well with little data. The goal is to improve the best estimation of the global consumption for a building.


Competition End Date:

March 31, 2018, 11:59 p.m. UTC

Place Prize Amount
1st €12,000
2nd €7,000
3rd €4,000

Note: Prizes delivered by DrivenData in USD, based on the exchange rate on February 6, 2018.