However, the accuracy of this technique is heavily dependent on an aggregated level of data.
The third category is separating the regular pattern from the other component of load profile such as uncertainty and noise by pre-processing techniques, mostly spectral analysis such as empirical mode decomposition (EMD) , Fourier transforms , and wavelet analysis .
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The second category is using the aggregated smart metering data to cancel out the uncertainty.
Therefore, the aggregated load exhibits typically regular patterns and more accessible to predict.
The latter uses the load values of the current and previous time steps as inputs to predict the load value at the subsequent time step.
A forecaster for the total load of the Australian national energy market was based on an ensemble of extreme learning machines (ELMs) is suggested in .Notwithstanding the above, using this IS does not constitute consent to PM, LE or CI investigative searching or monitoring of the content of privileged communications, or work product, related to personal representation or services by attorneys, psychotherapists, or clergy, and their assistants.Such communications and work product are private and confidential. Consequently, the forecasting of household energy consumption is crucial for household DSR programs.Precise short-term load forecasting (STLF) has a significant effect on the accuracy of the household DSR.A committed input choice structure to work with the hybrid prediction framework using the Bayesian neural network and wavelet transformation was introduced in .