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1.6 KiB
1 line
1.6 KiB
1. Objective:
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We want to predict the power output of the power plant given other measured factors.
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2.License: Free to use, with citation request.
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3.Data Source: UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant)
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4. Data Set Information:
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The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set to work with full load.
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A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST), and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the vacuum is collected from and has an effect on the steam turbine, the other three of the ambient variables affect the GT performance.
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5.Field Meanings:
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A. Temperature: hourly average ambient temperature in Celsius (between 1.81 and 37.11 °C),
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B. Pressure: ambient pressure in millibar (between 992.89 and 1033.30 mbar),
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C. Humidity: relative percent humidity (between 25.56 and 100.16 %),
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D. Energy_Output: net hourly output of the plant in megawatts (between 420.26 and 495.76 MW),
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E. Vacuum: exhaust vacuum in cm Hg (between 25.36 and 81.56 cm Hg).
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6.Parameter Selection:
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A.Dashboard: Predict Numeric Fields
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Settings:
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1) Field to predict: Energy_Output
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2) Fields to use for predicting: Temperature, Pressure, Humidity, Vacuum
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B. Dashboard: Detect Numeric Outliers
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Settings:
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1) Field to analyze: Humidity
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2) Threshold method: Standard Deviation
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3) Threshold multiplier: 3
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4) No sliding window
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