L. Ghods, M. Kalantar,
Volume 6, Issue 3 (September 2010)
Abstract
Prediction of peak loads in Iran up to year 2011 is discussed using the Radial
Basis Function Networks (RBFNs). In this study, total system load forecast reflecting the
current and future trends is carried out for global grid of Iran. Predictions were done for
target years 2007 to 2011 respectively. Unlike short-term load forecasting, long-term load
forecasting is mainly affected by economy factors rather than weather conditions. This
study focuses on economical data that seem to have influence on long-term electric load
demand. The data used are: actual yearly, incremental growth rate from previous year, and
blend (actual and incremental growth rate from previous years). As the results, the
maximum demands for 2007 through 2011 are predicted and is shown to be elevated from
37138 MW to 45749 MW for Iran Global Grid. The annual average rate of load growth
seen per five years until 2011 is about 5.35%
L. Ghods, M. Kalantar,
Volume 7, Issue 4 (December 2011)
Abstract
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.
