The forecasting of China oil demand based on fusion of soft computing and hard computing

Nowadays, with the high speed development of China's economy, being an important strategic resource, oil plays a more and more important role in economy development. This paper applies the fusion of soft computing and hard computing to the forecasting of China's oil demand from 2004 to 2015 year. Firstly, it sets up the main indexes that affect oil demand, viz. GDP, the gross population, resident consume level, the gross energy consume and the oil consumption. In analyzing the data feature of these indexes, we will obtain their jack-up trend of time series. According to this conclusion, the fusion of soft computing and hard computing will be applied: using Compertz curve of hard computing to forecast the value of these five indexes from 2004 to 2015, and then, based on these data, applying BP neural network of soft computing in forecasting the oil demand from 2004 to 2015 in China. Finally, weighted average of the oil demand from HC forecasting and that from SC forecasting is the paper's final forecasting result.