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Hourly Temperature Forecasting Using Abductive Networks R. E. Abdel-Aal

Summary: Review
Hourly Temperature Forecasting Using Abductive Networks
R. E. Abdel-Aal
Center for Applied Physical Sciences, Research Institute,
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Hourly temperature forecasts are important for electrical load forecasting and other
applications in industry, agriculture, and the environment. Modern machine learning techniques
including neural networks have been used for this purpose. We propose using the alternative
abductive networks approach, which offers the advantages of simplified and more automated
model synthesis and transparent analytical input-output models. Dedicated hourly models were
developed for next-day and next-hour temperature forecasting, both with and without extreme
temperature forecasts for the forecasting day, by training on hourly temperature data for five
years and evaluation on data for the 6
year. Next-day and next-hour models using extreme
temperature forecasts give an overall mean absolute error (MAE) of 1.68 F and 1.05 F,
respectively. Next-hour models may also be used sequentially to provide next-day forecasts.


Source: Abdel-Aal, Radwan E. - Computer Engineering Department, King Fahd University of Petroleum and Minerals


Collections: Computer Technologies and Information Sciences; Power Transmission, Distribution and Plants