The prediction of regional sulfur dioxide (SO2) emission of thermal power belongs to gray system which has small amounts of samples and little information, so a appropriate forecasting method is essential. Based on thermal power industry SO2 emission data from state department authorities, considering the main factors of China's thermal power industry SO2 predicted emission, we established a co...

With the expansion of wind farm installations in most countries all over the world, the power generation has already significantly influenced on the stability and security of the power grid after gridconnection. Wind power forecasting is an effective method for guarantees stability of the power output from wind farm. This paper proposed an improved GM(1,1) based prediction method, and focuses o...

Successful integration of renewable energy sources like wind power into smart grids largely depends on accurate prediction of power from these intermittent sources. Production of wind power cannot be controlled as the wind speed can vary based on weather conditions. Accurate prediction of wind power can assist smart grid that intelligently decides on the usage of alternative power sources based...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

Short-term power prediction of the photovoltaic system is one of the effective means to reduce the adverse effects of photovoltaic power on the grid. Since the efficiency of the traditional support vector machine (SVM) prediction method is low, this paper proposes the SVM based on the parameter optimization method of quantum particle swarm optimization (QPSO), and then apply into the power shor...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

In this paper, models for shortand long-term prediction of wind farm power are discussed. The models are built using weather forecasting data generated at different time scales and horizons. The maximum forecast length of the short-term prediction model is 12 h, and the maximum forecast length of the long-term prediction model is 84 h. The wind farm power prediction models are built with five d...

The large-scale integration of wind generation imposes several difficulties in the management of power systems. Wind power forecasting up to a few days ahead contributes to a secure and economic power system operation. Prediction models of today are mainly focused on spot or probabilistic predictions of wind power. However, in many applications, endusers require additional tools for the on-line...