Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I. (2017). Development of an Artificial Neural Network Model for Daily Electrical Energy Management. Proceedings of the 2nd International Engineering Conference, (IEC 2017), Federal University of Technology Minna, Nigeria, pp 120-125.
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Date
2017-05-07
Journal Title
Journal ISSN
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Publisher
Federal University of Technology Minna
Abstract
Efficient monitoring and control of electrical energy do not only prevent fire out-breaks caused by electrical appliances, but can also reduce excessive billings and prevent electrical installations. Most Energy Management Systems (EMS) for remote controlling of electrical appliances rely mostly on sensors, data and GSM networks which are un-reliable or even un-available in most part of developing world, this makes them less reliable. Therefore, there is need for an intelligent system that can manage electrical consumption intelligently using user-appliance interactive pattern over a period of time for intelligent control of users’ appliances in his/her absence. The model parameters (number of neurons and training algorithms) that affects its performance were first investigated and adopted. The performance of the developed model was evaluated using Regression analysis (R) and Mean Square Error (MSE) using ANN and Simulink tool boxes in Matlab R2015b. A good model can be used for real time control when deployed. Also, Scale Conjugate Gradient (SCG) training algorithm should also be used because of its high performance for pattern recognition problems. This work will go a long way in efficiently controlling household electrical appliances in the absence of the users thereby preventing fire disasters caused by electrical appliances, reducing the tariffs of consumers while increasing lifespan of electrical installations.
Description
This paper presents the development of an intelligent energy management system (EMS) designed to efficiently monitor and control household electrical appliances by learning user-appliance interaction patterns over time, especially for use in regions where conventional sensor and GSM-based EMS are unreliable or unavailable. Unlike existing systems, the proposed model leverages artificial neural networks (ANN) trained using the Scale Conjugate Gradient (SCG) algorithm to recognize usage patterns and enable smart control in the user's absence. Model parameters such as the number of neurons and training algorithms were optimized, and performance was evaluated using regression analysis and mean square error (MSE) in MATLAB Simulink. The system aims to prevent fire outbreaks, reduce excessive billing, and extend the lifespan of electrical installations, offering a practical and intelligent solution for energy control in developing regions.
Keywords
Artificial Neural Network, Electrical Energy Management, Energy Management System, Pattern Recognition
Citation
Abdullahi, I. M., Salawu B. T., Maliki D., Nuhu B K., & Aliyu, I. (2017). Development of an Artificial Neural Network Model for Daily Electrical Energy Management. Proceedings of the 2nd International Engineering Conference, (IEC 2017), Federal University of Technology Minna, Nigeria, pp 120-125.