Abstract
Accurate short term load forecasting (STLF) is a necessary part of resource management for a power generation company. The more precise the hourly load forecast, the more closely the power generation assets of the company can be configured to minimize the cost. Significant forecasting errors can lead to either overly conservative or overly risky scheduling, which can in turn induce heavy economic penalties [1]. Deregulation and consequent increase in competition makes a company’s ability to accurate forecasts an important contributor to its future success [2]. Automating the load forecasting process is a profitable goal and neural networks provide an excellent means of doing the automation [3].
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