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Neural networks can learn to approximate any function and behave like associative memories by using just example data that is representative of the desired task. They are model free estimators and are capable of solving complex problems based on the presentation of a large number of training data. This gives them a key advantage over traditional approaches to function estimation such as the statistical methods. Neural networks estimate a function without a mathematical description of how the outputs functionally depend on the inputs - they represent a good approach that is potentially robust and fault tolerant. In this thesis, we 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 TellFuture system. This system performs very well for short-term
gas load forecasting. The forecast
accuracy has been
in excess of 90%. In order to forecast the future load from the trained networks, we need to use the history loads, temperature, wind velocity, and calendar information 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. It has also been shown by other researchers that multi-layer feed-forward neural network performs best for short-term load forecasting [14][21]. We utilize only temperature, wind and calendar information since they are the only information available to us. Use of additional variables such as cloud coverage and economic information should yield better results [14]. Since the neural networks simply interpolate among the training data, it will give high errors with the test data that is not close enough to any one of the training data. 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 sale forecasting [22][23] as long as correct relationships between the inputs and the outputs can be captured and put in the models. But there is no universal network paradigm suitable for all kinds of forecasting problems. For each problem, a detailed analysis of domain data and the acquisition of prior knowledge are necessary to find a suitable model. The introduction of prior knowledge in input selection, input encoding, or architecture determination is often very useful, especially when the available domain data is limited. The standard back-propagation algorithm for training feed-forward neural networks has proven robust even for difficult problems. However, its high performance results are attained at the expense of a long training time to adjust the network parameters, which can be discouraging in many real-world applications. Even on relatively simple problems, it often requires a lengthy training process in which the complete set of training examples is processed hundreds or thousands of times. Thus some accelerating techniques or advanced training algorithms (Section 3.4.2) can be applied to improve the performance of the networks. |
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