SHORT TERM SEASONAL FORECASTING OF ELECTRICITY DEMAND USING SOFT INTELLIGENT HYBRID MODELS

Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of the decisions that generally has direct and non-strict relationship with the quality of decisions. This is the most important reason that why endeavor for improving the forecasting accuracy has never been stopped in the literature. Electricity demand forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of electricity demand is essential for careful planning and implementation of the necessary policies. For this reason electricity demand forecasting is important for financial and operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult forecasting in comparison with other commodity is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets data. Computational intelligence and soft computing approaches are among the most precise and useful methods for modeling the complexity and uncertainty in data. In this paper a soft intelligent method by combining mentioned methods is proposed in order to electricity demand forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems. Empirical results indicate that proposed model can achieve more accurate results rather than its component (Seasonal auto-regressive Integrated Moving Average models, artificial neural network) and also other current single forecasting methods such as classic regression, Seasonal Auto-Regressive Integrated Moving Average-fuzzy models and support vector machine