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https://github.com/samamasaleem/artificial-neural-network-for-combined-cycle-power-plant-energy-output-prediction
This project demonstrates the implementation of an Artificial Neural Network (ANN) model to predict the net hourly electrical energy output of a Combined Cycle Power Plant (CCPP).
https://github.com/samamasaleem/artificial-neural-network-for-combined-cycle-power-plant-energy-output-prediction
artificial-neural-networks combined-cycle-power-plant deep-learning keras machine-learning sklearn tensorflow uci-machine-learning
Last synced: 5 days ago
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This project demonstrates the implementation of an Artificial Neural Network (ANN) model to predict the net hourly electrical energy output of a Combined Cycle Power Plant (CCPP).
- Host: GitHub
- URL: https://github.com/samamasaleem/artificial-neural-network-for-combined-cycle-power-plant-energy-output-prediction
- Owner: SamamaSaleem
- Created: 2024-08-24T11:11:37.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-25T09:17:33.000Z (2 months ago)
- Last Synced: 2024-11-03T04:02:51.307Z (5 days ago)
- Topics: artificial-neural-networks, combined-cycle-power-plant, deep-learning, keras, machine-learning, sklearn, tensorflow, uci-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Artificial Neural Network for Combined Cycle Power Plant Energy Output Prediction
This project demonstrates the implementation of an Artificial Neural Network (ANN) model to predict the net hourly electrical energy output of a Combined Cycle Power Plant (CCPP). The steps below detail the process from importing libraries and data, to training the model and evaluating its performance.
## Table of Contents
- [Installation](#installation)
- [Dataset](#dataset)
- [Implementation](#implementation)
- [Importing the Libraries](#importing-the-libraries)
- [Importing the Dataset](#importing-the-dataset)
- [Splitting the Dataset](#splitting-the-dataset)
- [Building the ANN](#building-the-ann)
- [Training the Model](#training-the-model)
- [Predicting Test Results](#predicting-test-results)
- [Evaluating the Model](#evaluating-the-model)
- [Results](#results)
## Installation
Ensure you have Python and the following libraries installed:
- pandas
- scikit-learn
- tensorflow
You can install the required libraries using pip:
```bash
pip install pandas scikit-learn tensorflow
```
## Dataset
The dataset used is `Folds5x2_pp.xlsx`, sourced from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/294/combined+cycle+power+plant).
## Implementation
### Importing the Libraries
```python
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
```
### Importing the Dataset
```python
dataset = pd.read_excel('Folds5x2_pp.xlsx')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
dataset.head()
```
### Splitting the Dataset
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
```
### Building the ANN
```python
# Initializing the ANN
ann = tf.keras.models.Sequential()
# Adding the input layer and the first hidden layer
ann.add(tf.keras.layers.Dense(units=12, activation='relu'))
# Adding the second hidden layer
ann.add(tf.keras.layers.Dense(units=12, activation='relu'))
# Adding the output layer
ann.add(tf.keras.layers.Dense(units=1))
```
*Architecture of an Artificial Neural Network with 12 neurons in each hidden layer for energy output prediction.*
### Training the Model
```python
# Compiling the ANN
ann.compile(optimizer='adam', loss='mean_squared_error')
# Training the ANN model on the Training set
ann.fit(X_train, y_train, batch_size=32, epochs=100)
```
### Predicting Test Results
```python
y_pred = ann.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test), 1)), 1))
```
### Evaluating the Model
```python
# R-Squared score
print(r2_score(y_test, y_pred))
```
## Results
This Artificial Neural Network model achieved a high R-Squared score of **0.9164** on the test set, demonstrating strong predictive performance for estimating the energy output of a Combined Cycle Power Plant based on environmental factors. This project highlights the effectiveness of deep learning techniques in handling complex regression tasks in energy management.