Abstract
Several electric power companies are now forecasting electric loads based on conventional methods. However, since the relationship between loads and factors influencing these loads is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most of papers deal with 24-h-ahead load forecasting or next day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves change rapidly on the forecast day, loads change greatly and forecast error would be going to increase. Typically, load forecasting can be long-term, medium-term, short-term or very short-term. This paper concentrates on short-term load forecasting and partially on medium-term load forecasting applying regression models.
Highlights
The operation of modern power systems is usually associated with a variety of operations planning procedures
Operations planning encompass methodologies and decision processes by which an electric power system is prepared to meet the electric load within a set of specified technical performance criteria as well as economic performance criteria
Computer program results for load forecasting of the Jordan power system are presented in Fig. 5 to 12
Summary
The operation of modern power systems is usually associated with a variety of operations planning procedures. The operations planning process must start with a projection of what the electric load will be at future time intervals of interest, i.e. load forecasting. The basic load forecasting model can be described as follows[2]: Consider a future time interval (tk, tk+1).
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