ELECTRICITY FORECASTING FOR SMALL SCALE POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

• Main Author: SOLAHUDDIN, SALWA
• 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/7324/1/2011%20-%20Electricity%20forecsating%20for%20small%20scale%20power%20system%20using%20artificial%20neural%20network.pdf; http://utpedia.utp.edu.my/7324/
• Institution: Universiti Teknologi Petronas

This project presents a practical short term load forecasting (STLF) for small scale power system using artificial neural network (ANN) method. The project applies a generic three-layered feedforward network. The network is trained in a supervised manner and used backpropagation as a learning teclmique. In addition, a configuration consisting of a hidden layer that uses a hyperbolic tangent sigmoid transfer function and the output layer with a pure linear transfer function is applied. Gas District Cooling (GDC) is chosen as a case study for small scale power system since this plant was designed to produce electrical power supply and chilled water for Universiti Teknologi PETRONAS (UTP) campus and in-plant use. As a sole customer of GDC power plant, the load data from 2006 till 20 I 0 are gathered and utilized for model developments. There are two models developed based on UTP normal operating semester (Semester On) and during break (Semester Off). The developed models can forecast electricity load for the one week ahead. The computation experimental of the proposed network applies MATLAB software and its toolbox. The mean absolute percentage error (MAPE) is used as the measurement for the forecasting performance. At the end of this project, the proposed method using ANN manages to get average MAPE of 6.72 % for Model I (Semester Off) and 3.92 % for Model 2 (Semester On) which is considered relatively good result.