Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
Abstract
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.
How to cite this document
Nose-Filho, K.; Lotufo, A. D P; Minussi, C. R.. Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter. 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. Available at: <http://hdl.handle.net/11449/72741>.
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