International Journal of Energy and Statistics

This paper proposes an interval-based model to investigate the relationship between crude oil prices and US stock market indices. We incorporate the point dummy variables with the proposed interval-based model to further explore the impact on this relationship caused by the sub-prime financial crisis. The economic significance and hypothesis tests for the parameters of interest are provided. Empirical results suggest that US stock market indices have a positive marginal effect on the crude oil prices in the short term which also remains during the sub-prime financial crisis. However, the lagged values of interval crude oil prices affect US stock market indices negatively before the sub-prime financial crisis, while the lagged values of interval crude oil prices exhibit a positive marginal effect on US stock market indices accompanied by the occurrence of the sub-prime financial crisis, which increases the volatility of US stock market indices.

This paper develops a new approach for producing probabilistic wind power forecasts using a single forecast. The Singular Spectrum Analysis technique is used as the forecasting technique. Given the confidence interval calculated for the single forecast, a large number of random forecasts were generated through the Monte Carlo method. The purpose of generating probabilistic wind power forecasts is to use the results in a stochastic programming unit commitment problem. Therefore, probabilistic forecasts are reduced to a small number of representative forecast scenarios by applying a scenario reduction algorithm. The stability of the scenario reduction algorithm is also evaluated. The results indicate that the insignificant changes of operational cost of the stochastic programming problem reflect a deletion of unimportant forecast scenarios.

The forecasting of electricity consumption and demand plays a pivotal role in electric power systems planning. This paper proposes the combination of forecasts from two approaches with the aim of improving the forecasting accuracy, in order to make the best use of the installed transmission and generating capacity. In the first approach, the consumption time series is decomposed by wavelet analysis and a Box-Jenkins model is fitted to each wavelet component, following which the individual components forecasts are added to compute the total consumption forecast. The alternative approach, uses the Singular Spectrum Analysis technique to model the consumption time series in order to shrink the noise level. Thereafter, the Box-Jenkins model is used to forecast the filtered time series, producing a second forecast for the consumption series. Eventually, the two forecasts are combined geometrically in order to minimize the mean square error. The proposed methodology is illustrated by a computational experiment with the time series of residential consumption of electricity in Brazil.

The incessantly growing demand for energy consumption and the significance of the availability of sustainable energy for achieving long term economic growth defines the importance of forecasting energy statistics. This paper analyses and forecasts actual energy consumption data for EU-27 nations using both parametric and nonparametric time series forecasting techniques. Singular Spectrum Analysis (SSA) is adopted as the nonparametric time series analysis and forecasting technique and the results from SSA are compared with ARIMA, which is a parametric forecasting technique.

The growth of demand in developing countries has given rise to a constant increase in consumption of most non-renewable resources, including oil and gas. In this regard, the importance of planning activities rises because of the limited availability of oil and gas resources. Optimization techniques are tools that help upstream and midstream managers to decide optimally. The purpose of this review article is to provide a summary of the scientific literature on optimization applications in oil-and-gas upstream and midstream management. The main problems are described within a classification scheme and the most important contributions are summarized.