Climate change is a globally relevant, urgent, and multi-faceted issue heavily impacted by energy policy and infrastructure. Addressing climate change involves mitigation (i.e. mitigating greenhouse gas emissions) and adaptation (i.e. preparing for unavoidable consequences). Mitigation of GHG emissions requires changes to electricity systems, transportation, buildings, industry, and land use.
According to a report issued by the International Energy Agency (IEA), the lifecycle of buildings from construction to demolition were responsible for 37% of global energy-related and process-related CO2 emissions in 2020. Yet it is possible to drastically reduce the energy consumption of buildings by a combination of easy-to-implement fixes and state-of-the-art strategies. For example, retrofitted buildings can reduce heating and cooling energy requirements by 50-90 percent. Many of these energy efficiency measures also result in overall cost savings and yield other benefits, such as cleaner air for occupants. This potential can be achieved while maintaining the services that buildings provide.
The WiDS Datathon 2022 focuses on a prediction task involving roughly 100k observations of building energy usage records collected over 7 years and a number of states within the United States. The dataset consists of building characteristics (e.g. floor area, facility type etc), weather data for the location of the building (e.g. annual average temperature, annual total precipitation etc) as well as the energy usage for the building and the given year, measured as Site Energy Usage Intensity (Site EUI). Each row in the data corresponds to the a single building observed in a given year. Your task is to predict the Site EUI for each row, given the characteristics of the building and the weather data for the location of the building.
You are provided with two datasets: (1) the training dataset where the observed values of the Site EUI for each row is provided and (2) the test dataset where we withhold the observed values of the Site EUI for each row. To participate in the Datathon, you will submit a solution file containing the predicted Site EUI values for each row in the test dataset. The predicted values you submit will be compared against the observed Site EUI values for the test dataset and this will determine your standing on the Leaderboard during the competition as well as your final standing when the competition closes.
Features
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id: building id
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Year_Factor: anonymized year in which the weather and energy usage factors were observed
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State_Factor: anonymized state in which the building is located
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building_class: building classification
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facility_type: building usage type
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floor_area: floor area (in square feet) of the building
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year_built: year in which the building was constructed
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energy_star_rating: the energy star rating of the building
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ELEVATION: elevation of the building location
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january_min_temp: minimum temperature in January (in Fahrenheit) at the location of the building
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january_avg_temp: average temperature in January (in Fahrenheit) at the location of the building
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january_max_temp: maximum temperature in January (in Fahrenheit) at the location of the building
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cooling_degree_days: cooling degree day for a given day is the number of degrees where the daily average temperature exceeds 65 degrees Fahrenheit. Each month is summed to produce an annual total at the location of the building.
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heating_degree_days: heating degree day for a given day is the number of degrees where the daily average temperature falls under 65 degrees Fahrenheit. Each month is summed to produce an annual total at the location of the building.
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precipitation_inches: annual precipitation in inches at the location of the building
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snowfall_inches: annual snowfall in inches at the location of the building
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snowdepth_inches: annual snow depth in inches at the location of the building
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avg_temp: average temperature over a year at the location of the building
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days_below_30F: total number of days below 30 degrees Fahrenheit at the location of the building
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days_below_20F: total number of days below 20 degrees Fahrenheit at the location of the building
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days_below_10F: total number of days below 10 degrees Fahrenheit at the location of the building
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days_below_0F: total number of days below 0 degrees Fahrenheit at the location of the building
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days_above_80F: total number of days above 80 degrees Fahrenheit at the location of the building
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days_above_90F: total number of days above 90 degrees Fahrenheit at the location of the building
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days_above_100F: total number of days above 100 degrees Fahrenheit at the location of the building
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days_above_110F: total number of days above 110 degrees Fahrenheit at the location of the building
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direction_max_wind_speed: wind direction for maximum wind speed at the location of the building. Given in 360-degree compass point directions (e.g. 360 = north, 180 = south, etc.).
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direction_peak_wind_speed: wind direction for peak wind gust speed at the location of the building. Given in 360-degree compass point directions (e.g. 360 = north, 180 = south, etc.).
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max_wind_speed: maximum wind speed at the location of the building
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days_with_fog: number of days with fog at the location of the building
Target
- site_eui: Site Energy Usage Intensity is the amount of heat and electricity consumed by a building as reflected in utility bills
The RF Regressor was able to outperformed other algorithms utilized in the project with a 98% prediction on validation data and a Rsquare of 31%.