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 colected from and has effect on the Steam Turbine, he other three of the ambient variables effect the GT performance.
For comparability with our baseline studies, and to allow 5x2 fold statistical tests be carried out, we provide the data shuffled five times.
For each shuffling 2-fold CV is carried out and the resulting 10 measurements are used for statistical testing.
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.
Optimization is a big part of machine learning. Almost every machine learning algorithm has an optimization algorithm at its core.
Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost).
Gradient descent is best used when the parameters cannot be calculated analytically or when you need an optimized way to calculate those parameters.
Intuition for Gradient Descent Suppose you are at the top of a mountain, and your goal is to reach a lake which is at the lowest point of the mountain.
A twist is that you are blindfolded, and you have zero visibility to see where you are headed.
The best way is to check the ground near you and observe where the land tends to descend. This will give an idea in what direction you should take your first step. If you follow the descending path, it is very likely you would reach the lake. Any position on the mountai n is the cost value evaluated by the current values of the coefficients (values of coefficients corresponding given by that point on bowl). The lake is the point where the cost will be minimum. The goal is to find this point.