Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
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Data
2011-10-05
Autores
Nose-Filho, K. [UNESP]
Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
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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.
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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
Como citar
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.