Short-term wind speed forecasting is useful for power system to regulate power dispatching plan, decrease reserve power needed and increase the reliability of system. The method of the short-term wind speed prediction is proposed in this paper. The data of wind speed in Dafeng, Jiangsu Province of China is predicted by the model combined with the grey system theory (GST). A grey system model co...

Wind energy is increasing its participation as a main source of energy in power grids and electric utility systems around the world. One of the main difficulties of integrating large amounts of wind energy in power grids is the natural intermittency of its generated power [1, 2] due to the energy produced from the wind turbine being dependent on the availability of the wind, which is highly sto...

Careful planning of the electrical power sector is of great importance since the decisions to be taken involves the commitment of large resources, with potentially serious economic risks for the electrical utility and the economy as a whole. There are different types of techniques available for analysis and prediction of randomly varying parameters. They are classified as statistical, intellige...

Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this pa...

In order to improve the safety of train operation, a short-term wind speed forecasting method is proposed based on a linear recursive autoregressive integrated moving average (ARIMA) algorithm and a non-linear recursive generalized autoregressive conditionally heteroscedastic (GARCH) algorithm (ARIMA-GARCH). Firstly, the non-stationarity embedded in the original wind speed data is pre-processed...

Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMA...

In this paper, we developed a new method to forecast 10minutes ahead wind speed based on Heteroscdastic Gaussian Process and investigated the impacts of this predicting on a mix-mode operated building. The forecasting result shows the mean absolute percentage error (MAPE) of 9.2%. The indoor air temperature, infiltration air change rate and cooling energy consumption varied 25% in average compa...

With the development of climate change and global warming, more and more severe tropical storms, hurricanes and typhoons tend to emerge[nature], leading devastating threat and damage to human lives and social productions. Accurate forecasting of storm track and intensity is of vital importance to help evacuation and hence decrease losses. However, the accuracy of storm forecast heavily depends ...

With the increase of wind power as a renewable energy source in many countries, wind speed forecasting has become more and more important to the planning of wind speed plants, the scheduling of dispatchable generation and tariffs in the day-ahead electricity market, and the operation of power systems. However, the uncertainty of wind speed makes troubles in them. For this reason, a wind speed f...

Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Due to the increasing penetration of distributed energy resources, the net load is more and more affected by these non-temperature weather factors. T...