Graduation Semester and Year




Document Type


Degree Name

Master of Science in Electrical Engineering


Electrical Engineering

First Advisor

Lee Wei-Jen


Today, internet becomes one of the most important resources for useful information. However, since the authentic of the information is difficult to verify, one also has to take precaution when getting information from the internet. Utility companies need to forecast their load for unit commitment scheduling and system planning. The traditional approach for neural network forecasting relies on the temperature forecasting information from a single source. The customer loads are closely correlated with the temperature. Therefore, the accuracy of the load forecasting is affected by the temperature forecasting errors. The objective of this thesis is to reduce the temperature forecasting errors by using artificial neural network (ANN) to preprocessing the temperature forecasting information from various resources. In this thesis, temperature information from five (5) websites have been used fro this process. Each website provides hourly forecast temperature of 15 days. A JAVA program is designed to extract the useful temperature information from each individual web site and record them into the database (MySQL). Depends upon the available data, an ANN is then used to forecast hour ahead and day ahead temperatures up to seven days. Through this preprocessing, better weather information is obtained to have more accurate load forecasting results.


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington