JCOM_V3_N4_RP2 K. Sunil Manohar Reddy G. Ravindra Babu S. Krishna Mohan Rao Journal on Computer Science 2347–6141 3 4 9 22 Neural Networks, Feed Forward Networks, Recurrent Networks, Network Learning, Layered Networks, Back Propagation This paper describes and evaluates several global optimization issues of Artificial Neural Networks (ANN) and their applications. In this paper, the authors examine the properties of the feed-forward neural networks and the process of determining the appropriate network inputs and architecture, and built up a short-term gas load forecast system - the Tell Future system. This system performs very well for short-term gas load forecasting, which is built based on various Back- Propagation (BP) algorithms. The standard Back-Propagation (BP) algorithm for training feed-forward neural networks have proven robust even for difficult problems. In order to forecast the future load from the trained networks, the history loads, temperature, wind velocity, and calendar information should be used in addition to the predicted future temperature and wind velocity. Compared to other regression methods, the neural networks allow more flexible relationships between temperature, wind, calendar information and load pattern. Feed-forward neural networks can be used in many kinds of forecasting in different industrial areas. Similar models can be built to make electric load forecasting, daily water consumption forecasting, stock and markets forecasting, traffic flow and product sales forecasting. December 2015 - February 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=4829