Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter
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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
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2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011.
