Forecasting and prediction applications in the field of power engineering

Within the field of power engineering, forecasting and prediction technique s underpin a number of applications such as fault diagnosis, condition moni toring and planning. These applications can now be enhanced due to the impr oved forecasting and prediction capabilities offered through the use of art ificial neural networks. This paper demonstrates the maturity of neural net work based forecasting and prediction through four diverse case studies. In each case study the authors have developed diagnostic, monitoring or plann ing applications (within the power engineering field) using neural networks and industrial data. The engineering applications discussed in the paper a re: condition monitoring and fault diagnosis applied to a power transformer ; condition monitoring and fault diagnosis applied to an industrial gas tur bine; electrical load forecasting; monitoring of the refuelling process wit hin a nuclear power station. For each case study the data sources, data pre paration, neural network methods and implementation of the resulting applic ation is discussed. The paper will show that the forecasting and prediction techniques discussed offer significant engineering benefits in terms of en hanced decision support capabilities.