Decision Forest machine learning algorithm is adopted to find out the features to affect the temperature of fueling valve and controller and to predict it.

1. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant Data
Analysis and Prediction Using
Spark
Manvi Chandra, mchandr2@calstatela.edu
Jongwook Woo, PhD, jwoo5@calstatela.edu
High-Performance Information Computing Center (HiPIC)
California State University Los Angeles

2. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
Machine Learning
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
Hydrogen Gas Power Plant Prediction
Model

3. High Performance Information Computing Center
Jongwook Woo
CSULA
Myself
Name: Manvi chandra
Experience:
 2012 -2014
– Programmer Analyst at Cognizant Technology Solutions
 2015-2016 - Present : Master’s in information system
 Exposed to Big Data Analytics
 Pursuing research in Big data analytics and machine learning
 2007-2011-Bachelor of Technology in Electronics and
Communication Engineering.

4. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
Machine Learning
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
Hydrogen Gas Power Plant Prediction
Model

5. High Performance Information Computing Center
Jongwook Woo
CSULA
Introduction To Big Data

6. High Performance Information Computing Center
Jongwook Woo
CSULA
Data Issues
Large-Scale data
Tera-Byte (1012), Peta-byte (1015)
– Because of web
– Sensor Data (IoT), Bioinformatics, Social
Computing, Streaming data, smart phone, online
game…
Cannot handle with the legacy approach
Too big
Un-/Semi-structured data
Too expensive
Need new systems
Non-expensive

7. High Performance Information Computing Center
Jongwook Woo
CSULA
Two Cores in Big Data
How to store Big Data
How to compute Big Data
Google
How to store Big Data
– GFS
– On non-expensive commodity computers
How to compute Big Data
– MapReduce
– Parallel Computing with multiple non-expensive
computers
• Own super computers

8. High Performance Information Computing Center
Jongwook Woo
CSULA
What is Hadoop?
8
Hadoop Founder:
Doug Cutting
Chief Architect at Cloudera

9. High Performance Information Computing Center
Jongwook Woo
CSULA
Definition: Big Data
Inexpensive frameworks that can
store a large scale data and process
it faster in parallel
Hadoop
–Non-expensive Super Computer
–You can build and run your applications

10. High Performance Information Computing Center
Jongwook Woo
CSULA
Alternate of Hadoop MapReduce
Limitation in MapReduce
Hard to program in Java
Batch Processing
– Not interactive
Disk storage for intermediate data
– Performance issue
Spark by UC Berkley AMP Lab
In-memory storage for intermediate data
10 ~ 100x faster than N/W and Disk

11. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
Machine Learning
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
Hydrogen Gas Power Plant Prediction
Model

12. High Performance Information Computing Center
Jongwook Woo
CSULA
Machine Learning
Subfield of computer science that evolved from
the study of pattern recognition and
computational learning theory in artificial
intelligence.
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.

13. High Performance Information Computing Center
Jongwook Woo
CSULA
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.

14. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
Machine Learning
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
Hydrogen Gas Power Plant Prediction
Model

15. High Performance Information Computing Center
Jongwook Woo
CSULA
Spark
In-Memory Data Computing
Faster than Hadoop MapReduce
Can integrate with Hadoop and its ecosystems
HDFS
HBase, Hive, Sequence files
New Programming with faster data sharing
Good in complex multi-stage applications
– Iterative graph algorithms, Machine Learning
Interactive query

16. High Performance Information Computing Center
Jongwook Woo
CSULA
Spark
RDDs, Transformations, and Actions
Spark
Streaming
real-time
Spark
SQL
MLLib
machine
learning
DStream’s:
Streams of RDD’s
SchemaRDD’s
DataFrames
RDD-Based
Matrices
Spark Cores
GraphX
(graph)
RDD-Based
Matrices
Spark
R
RDD-Based
Matrices

17. High Performance Information Computing Center
Jongwook Woo
CSULA
Spark Drivers and Workers
Drivers
Client
–with SparkContext
• Create RDDs
Workers
Spark Executor
Run on cluster nodes
–Production
Run in local threads
–development

18. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
 Hive Examples
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
 Hydrogen Gas Power Plant Prediction
Model

19. High Performance Information Computing Center
Jongwook Woo
CSULA
RDD
Resilient Distributed Dataset (RDD)
Distributed collections of objects
–that can be cached in memory
RDD, DStream, SchemaRDD, PairRDD
Immutable
Lineage
–History of the objects
–Automatically and efficiently recompute lost
data

20. High Performance Information Computing Center
Jongwook Woo
CSULA
RDD Operations
Transformation
Define new RDDs from the current
–Lazy: not computed immediately
map(), filter(), join()
Actions
Return values
count(), collect(), take(), save()

21. High Performance Information Computing Center
Jongwook Woo
CSULA
Programming in Spark
Scala
Functional Programming
–Fundamental of programming is function
• Input/Output is function
No side effects
–No states
Python
Legacy, large Libraries
Java

22. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
 Hive Examples
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
 Hydrogen Gas Power Plant Prediction
Model

23. High Performance Information Computing Center
Jongwook Woo
CSULA
Spark
SparkSQL
Turning an RDD into a Relation
Querying using SQL
Spark Streaming
DStream
– RDD in streaming
– Windows
• To select DStream from streaming data
MLib
Sparse vector support, Decision trees, Linear/Logistic
Regression, PCA
SVD and PCA

24. High Performance Information Computing Center
Jongwook Woo
CSULA
Spark
Hydrogen gas power plant spark
model
o Separating the labeled column.
o Creation of RDD.
o Splitting the data into training and test sets.
o Training the dataset using Decision forest
regression algorithm.
o Evaluation of the result.

25. High Performance Information Computing Center
Jongwook Woo
CSULA
Spark
Hydrogen gas power plant spark
model

26. High Performance Information Computing Center
Jongwook Woo
CSULA
Contents
 Myself
 Introduction To Big Data
 Hive Examples
 Spark Cores
 RDD
 Spark SQL, Streaming, ML
 Hydrogen Gas Power Plant Prediction
Model

27. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
The Cal State L.A. Hydrogen Research
and Fueling Facility (H2 Station) was
formally opened on May 7, 2014.

28. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
The station is capable of producing hydrogen
onsite from renewable energy sources, using the
process known as electrolysis.
Cal State L.A. Hydrogen Research and Fueling
Facility became the first station in the nation
to sell hydrogen fuel by the kilogram to the
public.

29. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
Workflow

30. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
Model

31. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
Results and observations

32. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
Results and observations
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.

33. High Performance Information Computing Center
Jongwook Woo
CSULA
Hydrogen Gas Power Plant
Prediction Model
Results and observations
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.

34. High Performance Information Computing Center
Jongwook Woo
CSULA
Question?

35. High Performance Information Computing Center
Jongwook Woo
CSULA
References
Hadoop, http://hadoop.apache.org
Apache Spark op Word Count Example
(http://spark.apach.org )
Databricks (http://www.databricks.com )