Hourly temperature forecasting using abductive networks

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 5 years and evaluation on data for the 6th 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. Performance compares favourably with neural network models developed using the same data, and with more complex neural networks, reported in the literature, that require daily training. Performance is significantly superior to naive forecasts based on persistence and climatology.