American Journal of Energy Research
ISSN (Print): 2328-7349 ISSN (Online): 2328-7330 Website: http://www.sciepub.com/journal/ajer Editor-in-chief: Apply for this position
Open Access
Journal Browser
Go
American Journal of Energy Research. 2017, 5(3), 103-113
DOI: 10.12691/ajer-5-3-5
Open AccessArticle

An Integrated Machine Learning Algorithm for Energy Management and Predictions

Daniel Abode1, and Samson Oyetunji1

1Electrical and Electronics Engineering, Federal University of Technology Akure, Akure, Nigeria

Pub. Date: December 29, 2017

Cite this paper:
Daniel Abode and Samson Oyetunji. An Integrated Machine Learning Algorithm for Energy Management and Predictions. American Journal of Energy Research. 2017; 5(3):103-113. doi: 10.12691/ajer-5-3-5

Abstract

Management of energy consumption from the demand side has been inefficient and this has inadvertently affected the efforts of energy management on the supply side. This paper describes how machine learning integrated with Internet of things can enhance energy consumption management on the consumer side. Sensor nodes were designed and installed to gather data including date, time, temperature, humidity, light intensity, human presence and state of the load point switches. The sensor nodes transferred the data collected to a google firebase cloud storage which stored the data. The data collected was used in MATLAB neural network toolbox to train a machine learning algorithm that can predict the states of the receptacles, the ambient condition and the energy consumption statistics. The results show 99.7% success and 0.3% failure in the prediction of the state of the receptacle, 98.9% success and 1.1% failure in the prediction of the light state. In addition, the performance of the trained network for solving the classification and the regression problems that were involved in the prediction of the states of the switches and receptacles, the ambient condition and energy consumption statistics were shown graphically. Graphical results were also developed to show the relationship between the energy consumption, time of the day, human presence, and temperature. Conclusion was drawn on how time of the day, human presence and temperature affected the energy consumption on the consumer side.

Keywords:
sensor temperature humidity light intensity toolbox energy consumption classification regression management consumption energy

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

Figure of 16

References:

[1]  Raphael A.W. Energy Efficiency and Mangement in Industries- a case study of Ghana's largest Industrial Area. Linkoping: Institute of Technology Linkoping University, 2012. LIU-IEI-TEK-A-12/01311-SE.
 
[2]  An Approach to Energy Saving and COst of Energy Reduction Using an Improved Efficient Technology. Abubakar K.A., Abba L.B., et al. 4, s.l.: Open Journal of Energy Efficiency, 2015, Vol. I.
 
[3]  Alex S., Vishwanathan S.V.N. Introduction to Machine Learning. Cambridge: Cambridge University Press, 2008.
 
[4]  Some Studies in Machine learning Using the Game Checkers. Arthur L.S. Stanford: IBM Journal of Reasearch and Development, 1959.
 
[5]  Using Machine Learning on Sensor Data. Alexandra M., Marko P., et al. 4, Ljubjana: Journal of Computing and Information Technology, 2010, Vol. I.
 
[6]  Machine Learning Applied to Sensor Data Analysis. Go T., Moe T., et al. 1, Yokogawa: Yokogawa Technical Report, 2016, Vol. 59.
 
[7]  Mitchell T. Machine learning. Louisiana: McGraw Hill, 1997.
 
[8]  Andrew N.G. Machine learning Stanford University. [Video] s.l.: Cousera, 2017.
 
[9]  Artificial Neural Networks: A Tutorial. Anil K.J., Jianchang M., Mohiuddin K.M.,. s.l.: ACM Digital Library, 1996, Vol. 29.
 
[10]  EspressIf System IOT Team. ESP8266EX Datasheet. s.l.: http://bbs.espressif.com/, 2015.