Artificial Neural Network Create an ANN Regression model to predict a Combined Cycle Power Plant’s electrical energy output.
Hadelin de Ponteves, an AI expert, walks you through a case study that demonstrates how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant in this free course.
The goal is to develop a data model that predicts the plant’s net hourly electrical energy output (EP) using available hourly average ambient variables.
Hands-on with Hadelin as he solves this complex, real-world Deep Learning challenge, which covers everything from data preprocessing to building and training an ANN using the Machine Learning library, Tensorflow 2.0, and Google Colab, a free, browser-based notebook environment that runs entirely in the browser.
Check out what awaits you when you enroll:
Part 1: Data Preparation
Adding the dataset
dividing the dataset into training and test sets
Part 2: Creating an ANN
Including the first hidden layer and the input layer
Including an output layer
Creating the ANN
Part 3: Educating the ANN
On the training set, train the ANN model
Predicting the test set’s results
More Information on Combined-Cycle Power Plants
A combined-cycle power plant is an electrical power plant that uses a Gas Turbine (GT) and a Steam Turbine (ST) in tandem to produce more electrical energy from the same fuel than a single cycle power plant.
The gas turbine compresses air and combines it with a highly heated fuel. The hot air-fuel mixture flows through the blades, spinning them. To generate electricity, the fast-spinning gas turbine drives a generator. The exhaust (waste) heat emitted by the gas turbine’s exhaust stack is used by a Heat Recovery Steam Generator (HSRG) system to generate steam, which spins a steam turbine. The steam turbine powers a generator.
What you’ll discover
- How to Use Python to Create an Artificial Neural Network
- How to Perform Regression
- How to Make Use of Google Colab
Are there any course prerequisites or requirements?
- Deep Learning Fundamentals
- Anyone interested in Machine Learning and Deep Learning should take this course.