Wind Resource Assessment and Forecast Planning with Neural Networks

Forecast Combining with Neural Networks

This paper investigates the use of Artificial Neural Networks (ANNs) to combine time series forecasts of stock market volatility from the USA, Canada, Japan and the UK. We demonstrate that combining with nonlinear ANNs generally produces forecasts which, on the basis of out-of-sample forecast encompassing tests and mean squared error comparisons, routinely dominate forecasts from traditional li...

Neural networks for financial forecast

Forecast of Iran’s Electricity Consumption Using a Combined Approach of Neural Networks and Econometrics

Electricity cannot be stored and needs huge amount of capital so producers and consumers pay special attention to predict electricity consumption. Besides, time-series data of the electricity market are chaotic and complicated. Nonlinear methods such as Neural Networks have shown better performance for predicting such kind of data. We also need to analyze other variables affecting electricity c...

The Short-term Wind Power Forecast Based on Phase-space Reconstruction and Neural Networks

The short-term forecast of the wind power of a wind farm is of great significance for the security and stability of a grid-connected generation system. An accurate forecast may reduce the spinning reserve of a grid while providing reliable references for operation dispatch of a wind farm. In order to improve the accuracy of short-term forecasts, introducing the phase-space reconstruction techni...

Wind resource assessment of Khuzestan province in Iran

In this research paper, a 10 minute period measured wind speed data at 10 m, 30 m, and 40 m heights are presented for one of the major provinces of Iran. Four stations in Khuzestan- Abadan, Hosseyneh, Mahshahr, and Shushtar- are analyzed to determine the potential of wind power generation in this province. From the primary evaluation and by determining mean wind speed and also the Weibull f...