Artificial Neural network regression model to predict the energy output in a combined cycle power plant.
Energy output of a combined cycle power plant is been predicted with the help of a regressor model build on an Artificial Neural Network (ANN). Initially the data is divided into dependent and independent varialbes and feature scaling is applied on the variables.The the dataset is split into trainig set and testing set in a ratio of 4:1. Then the ANN model is built with google tensorfolw 2.7.0, keras and the model is then trained with the traning set.
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. We provide the data both in .ods and in .xlsx formats.
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 teh 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.
R2 score the model turns out to be = 0.943900808510774
For more information on the dataset visit: http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant