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RSS FeedsEnergies, Vol. 12, Pages 149: Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting (Energies)

 
 

3 january 2019 04:00:08

 
Energies, Vol. 12, Pages 149: Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting (Energies)
 




Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a lack of validity because it neglects several characteristics of the electric load exhibited by time series. In this work, we propose a load-forecasting model based on enhanced-LSTM that explicitly considers the periodicity characteristic of the electric load by using multiple sequences of inputs time lags. An autoregressive model is developed together with an autocorrelation function (ACF) to regress consumption and identify the most relevant time lags to feed the multi-sequence LSTM. Two variations of deep neural networks, LSTM and gated recurrent unit (GRU) are developed for both single and multi-sequence time-lagged features. These models are compared to each other and to a spectrum of data mining benchmark techniques including artificial neural networks (ANN), boosting, and bagging ensemble trees. France Metropolitan’s electricity consumption data is used to train and validate our models. The obtained results show that GRU- and LSTM-based deep learning model with multi-sequence time lags achieve higher performance than other alternatives including the single-sequence LSTM. It is demonstrated that the new models can capture critical characteristics of complex time series (i.e., periodicity) by encompassing past information from multiple timescale sequences. These models subsequently achieve predictions that are more accurate.


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50 viewsCategory: Biophysics, Biotechnology, Physics
 
Energies, Vol. 12, Pages 150: Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods (Energies)
Energies, Vol. 12, Pages 148: Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks (Energies)
 
 
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