BP neural network with optimum hidden layer nodes were used to evaluate the power environmental issues in China. Furthermore, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to optimise the number of hidden layer nodes of BP neural network. The evaluation...

Energy is the basis of social development. Practices show that national and regional economic development speed and degree are more obviously depending on the construction of modern energy supply support system. As one of the biggest developing countries of the world, the remarkable achievement...

Affected by various environment factors, wind speed presents characters of high fluctuations, autocorrelation and stochastic volatility; thereby it is hard to forecast with a single model. A hybrid model combining with input selected by deep quantitative analysis, Wavelet Transform (WT), Genetic...

This paper presents decomposition models for the change in carbon productivity, which is considered a key indicator that reflects the contributions to the control of greenhouse gases. Carbon productivity differential was used to indicate the beginning of decomposition. After integrating the...

Mitigating the impact of developing countries to global climate change has become an important issue in the fields of science and politics. This study proposes a small-sample hybrid model for forecasting the energy-related CO2 emissions of developing countries. The CO2 emissions of these...

CO2 emissions from fossil fuel combustion have been known to contribute to the greenhouse effect. Research on emission trends and further forecasting their further values is important for adjusting energy policies, particularly those relative to low carbon. Except for a few countries, the main...

For the period of the 12th Five-Year Plan (2011–2015), the Chinese government has decided to reconsider and adjust its policies on economic development because of the pressures of CO2 emissions and fossil energy consumption. The current paper adopts the logarithmic Stochastic Impacts by...

Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful...

Due to the excess consumption of natural resources and the severe deterioration of the environment, people are having a growing consciousness of environmental protection and more concerned of air pollution problems. However, the thermal power plant, as one of the main pollution sources, burns a...

Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an...