Paper Info
Title
Machine Learning with Big Data An Efficient Electricity Generation Forecasting System.
Abstract
Machine Learning (ML) is a powerful tool that can be used to make predictions on the future nature of data based on the past history. ML algorithms operate by building a model from input examples to make data-driven predictions or decisions for the future. The growing concept “Big Data” has brought much success in the field of data science; it provides data scalability in a variety of ways that empower data science. ML can also be used in conjunction with Big Data to build effective predictive systems or to solve complex data analytic problems. In this work, we propose an electricity generation forecasting system that could predict the amount of power required at a rate close to the electricity consumption for the United States. The proposed scheme uses Big Data analytics to process the data collected on power management in the past 20 years. Then, it applies a ML model to train the system for the prediction stage. The model can forecast future power generation based on the collected data, and our test results show that the proposed system can predict the required power generation close to 99% of the actual usage. Our results indicate that the ML with Big Data can be integrated in forecasting techniques to improve the efficiency and solve complex data analytic problems existing in the power management systems.
Year
DOI
Venue
2016
10.1016/j.bdr.2016.02.002
Big Data Research
Keywords
Field
DocType
Artificial neural network,Backpropagation,Big Data,Electricity generation forecast,Hadoop,MapReduce
Data science,Data mining,Computer science,Complex data type,Artificial intelligence,Artificial neural network,Power management,Electricity,Big data,Electricity generation,Abstract machine,Machine learning,Scalability
Journal
Volume
ISSN
Citations 
5
2214-5796
7
PageRank 
References 
Authors
0.58
7
3
Name
Order
Citations
PageRank
Mohammad Naimur Rahman170.58
Amir Esmailpour2478.90
Junhui Zhao392.71