Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter - Nose-Filho, K. [UNESP]; Lotufo, A. D P [UNESP]; Minussi, C. R. [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2011)
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.
Keywords: 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