Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
| Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
|---|---|
| Data(s) |
27/05/2014
27/05/2014
05/10/2011
|
| Resumo |
This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE. |
| Identificador |
http://dx.doi.org/10.1109/PTC.2011.6019428 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. http://hdl.handle.net/11449/72741 10.1109/PTC.2011.6019428 2-s2.0-80053350091 |
| Idioma(s) |
eng |
| Relação |
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011 |
| Direitos |
closedAccess |
| Palavras-Chave | #Artificial Neural Networks #Moving Average Filter #Short Term Load Forecasting #Signal Processing #Training Dataset #Abnormal data #Electrical substations #Filter-based #General regression neural network #Load data #Load forecasting #Missing data #Moving average filter #New zealand #Forecasting #Neural networks #Signal processing #Sustainable development #Electric load forecasting |
| Tipo |
info:eu-repo/semantics/conferencePaper |
