#TITLE_ALTERNATIVE# - Oloan Siregar NIM : 23200540, Baginda
Abstract Neural networks are computational models with the capacity to learn, to generalize, or to organize data based on parallel processing. Among all kinds of networks, the most widely used are multi-layer feed-forward neural networks that are capable of representing non-linear functional mappings between inputs and outputs and are hailed as Universal Approximators. These networks can be trained with a powerful and computationally efficient method called error back-propagation. In this thesis, a multi-layer feed-forward and reccurent neural network based electric load forecast model . It is known that electric load depends on many factors such as weather, calendar, and other economic information. The model will capture those effects, reflect them within the system, and provide valuable future forecasting data. Similar models can be built to solve problems in other fields as long as the correct relationship between the inputs and the outputs can be captured. The network consists of one input layer, one output layer and one hidden layer. Obviously, there is only one output unit - the electric load. The number of input units is also fixed, depends on how many factors are included in the model, and how the factors are encoded. The number of hidden units needs to be decided by training with some test sets. In this thesis we can use network configuration to find the optimal number of hidden units.This is a trail and error process.We can start with a large number of epoch an small number of hidden units and we can also use this setup to study the influence of the learning rate and momentum. The training result that the use back-propagation algorithm gives better for electric load forecast compared with the use statistical method and its approach has reduced the MAPE of hourly with 24-hour ahead . Similar reduction for two,three and four hours ahead. The reduction of the MAPE daily load electric forecast using one hour ahead is even much more significant in comparison with the one day ahead forecast.