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In soft sensor applications, the prediction using only relevant variables significantly improves model accuracy and decreases computational costs. This paper proposed a new method for variable selection based on random vector functional-link (RVFL) neural network model. This method removes input nodes from variable set according to an exclusion criterion by backward selection. Then the remaining weights...
Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes. We introduce a new model selection technique based on Gap statistics to learn the appropriate number of AR filters needed to model...
This paper presents three methods for the mapping model for provincial wind power prediction. After correlation analysis of the historical data, several wind farms' output power are found to be principal related to the global provincial wind power. For the first method, curve fitting and weighted average values are used to establish the mapping model. The second method is based on multiple linear...
This paper investigates the impact of wind direction on the prediction of wind power. A novel short-term wind power forecasting model is developed based on the data of wind speed, wind direction, wind farm output and numerical weather prediction. Statistical methods are adopted for the data analysis. To show the effectiveness, the approach is applied on a wind farm in north China. The average RMSE...
A regional resource information management system was developed to improve the water utilization and distribution in regional water systems. This paper took Langao village of Longkou city as an example to optimize allocation of water resources. The system integrates rainfall amount frequency analysis model, industrial, living and ecological environment water demand real-time forecast model, water...
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