Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant
Free tutorial
43,540 students
1hr 11min of on-demand video
Created by Hadelin de Ponteves, Ligency I Team, Ligency Team
English
English [Auto]
What you’ll learn
- How to implement an Artificial Neural Network in Python
- How to do Regression
- How to use Google Colab
Requirements
- Deep Learning Basics
Description
Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.
The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.
Go hands-on with Hadelin in solving this complex, real-world Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will supercharge your Machine Learning toolkit.
Check out what’s in store for you when you enroll:
Part 1: Data Preprocessing
- Importing the dataset
- Splitting the dataset into the training set and test set
Part 2: Building an ANN
- Initializing the ANN
- Adding the input layer and the first hidden layer
- Adding the output layer
- Compiling the ANN
Part 3: Training the ANN
- Training the ANN model on the training set
- Predicting the results of the test set
More about Combined-Cycle Power Plants
A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.
The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.
Who this course is for:
- Anyone interested in Machine Learning and Deep Learning
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Course content
5 sections • 13 lectures • 1h 12m total lengthCollapse all sections
Introduction4 lectures • 14min
- Getting started09:19
- Dataset + Code + Colab Link00:11
- Study Tips for Success00:46
- Importing the libraries03:15
Part 1 – Data Preprocessing2 lectures • 17min
- Importing the dataset10:45
- Splitting the dataset into the Training set and Test set05:58
Part 2 – Building the ANN3 lectures • 18min
- Initializing the ANN06:14
- Adding the first layers08:21
- Adding the output layer03:15
Part 3 – Training the ANN3 lectures • 24min
- Compiling the ANN05:21
- Training the ANN model on the Training set08:07
- Predicting the results of the Test set10:19
BONUS Lectures1 lecture • 1min
- YOUR SPECIAL BONUS01:06