We have created a model using decision forest regression which would predict vehicle pressure.

1. ANALYSIS OF DATA FROM
HYDROGEN GAS POWER
PLANT USING MACHINE
LEARNING
By Manvi Chandra

2. MACHINE LEARNING
Machine learning is a subfield of computer science that evolved from the
study of pattern recognition and computational learning theory in artificial
intelligence.
Machine Learning explores pattern recognition during data analysis through
computer science and statistics.
Machine learning is a method of data analysis that automates analytical
model building. Using algorithms that iteratively learn from data, machine
learning allows computers to find hidden insights without being explicitly
programmed where to look.

3. TYPES OF MACHINE LEARNING ALGORITHMS

4. MACHINE LEARNING STUDIO
Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop
tool you can use to build, test, and deploy predictive analytics solutions on
your data.

5. WORKFLOW
Data Gathering
Load the data
set
Clean the data
set
Split the data
Train ModelApply
Algorithm
Evaluate Model

6. MODEL FOR HYDROGEN GAS POWER PLANT
DATA

7. RESULTS AND ANALYSIS

8. RESULTS AND ANALYSIS CONTINUED
 According to our research we are able to predict Vehicle Pressure (Pressure of
hydrogen gas within the vehicle Hydrogen Storage System)using our model.
The algorithm used is decision forest regression.
Decision forest are an ensemble learning method for classification, regression and
other tasks, that operate by constructing a multitude of decision trees at training
time and outputting the class that is the mode of the classes (classification) or mean
prediction (regression) of the individual trees.

9. RESULTS AND ANALYSIS CONTINUED
STATE OF CHARGE (SOC):-
Ratio of hydrogen density within the vehicle storage system to the full-fill density.
SOC is expressed as a percentage and is computed based on the gas density as per
formula below:-
Our model predict vehicle pressure which in turn could be used to determine the
state of charge.

10. Thank You