Weather sensitive short-term load forecasting using artificial neural networks for the LEYECO II distribution system - Gumiran, Brian Anthony L. (2011)
This paper presents the development of a short term load forecast (STFL) tool that forecasts up to one-week-ahead hourly loads. Historical temperature and humidity date were obtained from PAGASA. To keep the load forecasts timely, the forecasted temperature and humidity data were obtained from an online weather forecasting service. The STFL tool was implemented for LEYECO II, which provided the historical load data. The STFL tool used artificial neural networks (ANNs) to model the relationship between the future load and relevant factors, such as previous load and weather variables. The ANN configuration of the STFL tool was improved to achieve the highest accuracy. In all, 17 different configurations were tested. The STFL tool also includes a user interface to read historical load data and produce the forecasts. The most accurate model achieved mean absolute percentage error (MAPE) of 2.9554% and an average MAPE of 4.6694%. The final model is a 4-ANN set that uses as inputs the previous hour load, previous and present hour temperature, present hour humidity and the expected present hour load value based on hourly load profiles.
Undergraduate student project (B.S. Electrical Engineering)--University of the Philippines, Diliman.
Keywords: Electric power consumption -- Forecasting -- Philippines; Neural networks (Computer science)