Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods - Pituk Bunnoon (International Journal of Electrical and Computer Engineering (IJECE), 2016)
One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.