gokriznastic / gradient-descent-linear-regression

Using Linear Regression and Stochastic Gradient Descent coded from scratch to predict the electrical energy output for a combined circle power plant.

CombinedCyclePowerPlant

Using Linear Regression and Stochastic Gradient Descent to predict the electrical energy output for a combined circle power plant.

Dataset Information

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. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant. 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 effect on the Steam Turbine, the other three of the ambient variables effect the GT performance.

Attribute Information

Features consist of hourly average ambient variables

• Temperature (T) in the range 1.81°C and 37.11°C,
• Ambient Pressure (AP) in the range 992.89-1033.30 milibar,
• Relative Humidity (RH) in the range 25.56% to 100.16%
• Exhaust Vacuum (V) in the range 25.36-81.56 cm Hg
• Net hourly electrical energy output (EP) 420.26-495.76 MW The averages are taken from various sensors located around the plant that record the ambient variables every second. The variables are given without normalization.

Classifier Used

Linear Regression using Stochastic Gradient Descent; both codes were written from scratch were used to predict the energy output for the given dataset.