Daniel Abode^1,, Samson Oyetunji¹

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.

sensor, temperature, humidity, light intensity, toolbox, energy consumption, classification, regression, management, consumption, energy