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Machine learning methods are main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey...
In recent years, various kinds of satellite-derived aerosol products have been used to air quality monitoring. However, satellites are not sensitive to the near surface aerosol which impacts human health but the entire aerosol column. In this paper, we establish an artificial neural network (ANN) instead of multiple regression technique to lessen the surface PM2.5 estimation uncertainty from remote...
Electric power system load forecasting plays an important role in the energy management system (EMS), which has great effect on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will lead to economic cost saving and right decisions on generating electric power. Electric power load is difficult to be forecasted accurately for...
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