Abstract
Electricity demand forecasting is an important task in power grids. Most of researches on electrical load forecasting have been done in the time domain. But, the electrical time series has a non-stationary inherence that makes hard load prediction. Moreover, valuable information is hidden in the electrical load sequence which is not open in the time domain. To deal with these difficulties, a new electricity demand forecasting framework is proposed in this work. In the proposed framework, at first, a new feature space of electrical load sequence is composed. The provided domain involves complementary information about shape and variations of electrical load sequence. Then, the obtained load features are integrated with the original load values in time domain to allow a rich input for predictor. Finally, a powerful deep learning technique from the family of recurrent neural networks, named long-short term memory, is used to learn electricity demand from the provided features in single and hybrid domains. The following domains are investigated in this work: frequency, cepstrum, spectral centroid, spectral roll-off, spectral flux, energy, time difference, frequency difference, Gabor and collaborative representation. The experiments show that the use of time difference domain decreases the mean absolute percent error from 0.0332 to 0.0056.
Highlights
An accurate electrical load prediction is necessary to build an intelligent energy management system, adjust and monitor energy demand and supply
long-short term memory (LSTM) has longer memory than RNN such that it is appropriate to learn from input samples and what experienced from past time with very long memory
The load data, as a temporal sequence or time series, contains worthful information about consumption behavior of customers in successive time intervals. This historical data in the time domain has been used for load forecasting in most of introduced Shortterm load forecasting (STLF) methods
Summary
An accurate electrical load prediction is necessary to build an intelligent energy management system, adjust and monitor energy demand and supply. The load sequence is considered as a signal or time series. This historical data in the time domain has been used for load forecasting in most of introduced STLF methods.
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