Two of the most significant challenges facing the world in the 21st century are improving energy security and mitigating the effects of climate change. To counter these challenges, renewable energy sources, such as wind, are considered a possible solution and have gained importance worldwide. With many jurisdictions setting high wind-energy targets for the coming decades, risks have grown as the demand for new wind turbines has outstripped the growth of its suppliers. Integrating significant amounts of wind-electricity into existing networks raises reliability concerns due to variable nature of wind. A method for estimating the reliability of wind-energy systems is presented which is a combination of a forecasting method (probabilistic approach) and RL (Resistance-Load) technique (risk-based approach), demonstrated through a case study, and verified using real-time wind farm data.

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in ITS technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in literature. However, forecasting reliability is not properly addressed in existing studies. Most forecasting methods only focus on the expected value of traffic flow, assuming constant variance when perform forecasting. This method does not consider the volatility nature of traffic flow data. This paper demonstrated that the variance part of traffic flow data is not constant, and dependency exists. A volatility model studies the dependency among the variance part of traffic flow data and provides a prediction range to indicate the reliability of traffic flow forecasting. We proposed an ARIMA-GARCH (Autoregressive Integrated Moving Average- AutoRegressive Conditional Heteroskedasticity) model to study the volatile nature of traffic flow data. Another problem of existing studies is that most methods have limited forecasting abilities when there is missing data in historical or current traffic flow data. We developed a General Regression Neural Network(GRNN) based multivariate forecasting method to deal with this issue. This method uses upstream information to predict traffic flow at the studied site. The study results indicate that the ARIMA-GARCH model outperforms other methods in non-missing data situations, while the GRNN model performs better in missing data situations.