NEURAL NETWORK PREDICTION MODEL OF ENERGY CONSUMPTION FOR BILLING INTEGRITY

• Main Author: ZAHARULHISHAM, NURUL HAMIZA
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2011
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/8253/1/2011%20-%20Neural%20network%20prediction%20model%20of%20energy%20consumption%20for%20billing%20integrity.pdf; http://utpedia.utp.edu.my/8253/

Neural networks for the real world applications are increasing rapidly. Artificial Neural Network is a system loosely modeled based on the human brain. It has ability to account for any functional dependency. The network discovers by learning and modeling the nature of the dependency without needing to be prompted. Nowadays, neural networks are a powerful technique to solve many real world problems. They have the ability to learn from experience in order to improve their performance and to adapt themselves to the changes in the environment. Furthermore, they are able to deal with incomplete information or noisy data and can be very effective especially in situations where it is not possible to define the rules or steps that lead to the solution of a problem. This report contains five chapters which are the introduction, literature review methodology, results and discussions and the conclusion. In the first chapter, the introduction explains about the background study, problem statement and also the main objectives of the project. The main objective of the project is to develop a neural network model to predict energy consumption for billing integrity. The second chapter of this report stated the theory and literature review of the neural network. The literature review is taken mostly from journals of many previous studies about neural network. Next, the third chapter explains about the methodology of the project. Under the methodology section, the author includes a project activities flow chart and also explains about the tools required to execute the project. This project is carried out in two semesters. The milestone for the project work is presented nicely in a Gantt chart. Then, in the results and discnssion chapter, the author stated about the neural network model developed using ¥-ATLAB. Last but not least, the conclusion recommendations for the model im11rovement.