Non-Stationary Signal Forecasting by Neural Network with Modified Neurons
This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). in this data set, the load information is the sum of power load consumed by three areas, including Allentown, Baltimore and Philadelphia. The historical load and temperature information from year 2003 to year 2008 were studied and simulated. The forecasts of one-dayahead daily total load and peak load were implemented. In order to find the accurate forecasting results, different combinations of inputs were carried out. In this study, mean absolute percentage error (MAPE) is used as the measurement of forecasting performances.
load forecasting neural model modified neurons
Chih-Chien Huang Yi-Ching Lin Yu-Ju Chen Shuming T. Wang Rey-Chue Hwang
Department of Electrical Engineering I-Shou University Kaohsiung County, Taiwan Department of Information Management Cheng Shiu University Kaohsiung County, Taiwan
国际会议
长沙
英文
1910-1913
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）