In world economy, crude oil is considered as one of the most strategic commodities playing a vital role in the determination of many regional and global equations. So, it is well known that an intense fluctuation of the oil price causes large recession in OPEC countries. So many researchers attempt to forecast crude oil price while oil market is one of the most complex, turbulent and chaotic international financial markets. In present research, gray system theory is utilized to model and forecast the price of crude oil. The results represent that gray forecasting model significantly improves the accuracy of the forecasting operation.

Forecasting methods have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecaster in such unstable environments .The superiority of grey models over conventional forecasting model is that grey models require only a limited amount of data to predict behaviors of systems without knowing their mathematical models. This paper investigated Grey models for forecasting crude oil price and compared accuracy of models. The results obtained shows that operating of Rolling Grey and Grey M (1, 1) are better than another using models.

In the last few decades, world financial markets have faced numerous volatilities and fluctuations. As a result active economic agents have become more interested in how to understand and foresee this fluctuation. Forecasting developments of the oil market has become ever more important after the oil crisis of the 1970s. The objective was to reduce the potential risk of such fluctuations by having advance knowledge of expected changes. Review of time series data of the West Texas Intermediate (WTI) prices indicate the presence of clustering volatility, can not be ignored in furcating. This has led us to focus in this study on the forecasting volatility of crud oil prices. We have used ARCH models to evaluate statistical errors of forecasts. The study results indicate continuous effect of shocks on conditional variance of crude oil prices. Future volatility of crude oil prices is dependent on oil price changes in the past. GARCH and TGARCH models perform better than other models of conditional variance in forecasting volatility of crude oil prices.

To forecast the electricity load of a city or country and facilitate the strategic decision-making, it is common to collect the historical data from different zones of the city or different cities of the country. However, normally all the zones or different sectors’ load (residential, industrial, and commercial) are not important equally. In other words, a certain zone or a sector may have the most effect on decision making. Therefore, the simple algebraic sum of the different zones’ forecasting may not be meaningful for the ultimate objective. There are different methods for aggregation of the different zones’ forecasts. The most convenient method is the simple algebraic sum of the different zones’ forecasts, which is not only inefficient but also needs more details about the effective factors on the electricity demand in each zone. In this paper, different aggregation approaches such as bottom-up, top-down, optimal combination methods are presented. It should be mentioned that any research paper in the field of the electrical power system and load forecasting have not studied the hierarchical forecasting; therefore, presenting the hierarchical method for load forecasting is a strict innovation of this paper. The Auto-Regressive Integrated Moving Average (ARIMA) and Exponential Smoothing methods are embedded in proposed aggregation approaches. The proposed methods are applied to forecast Australian electric load in short-term and long-term horizons.