Long short-term memory-singular spectrum analysis-based model for electric load forecasting

2020 ◽  
Author(s):  
Neeraj Neeraj ◽  
Jimson Mathew ◽  
Mayank Agarwal ◽  
Ranjan Kumar Behera
Keyword(s):  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Electric Load ◽  
Short Term ◽  
Term Memory ◽  
Singular Spectrum ◽  
Long Short Term Memory ◽  
Electric Load Forecasting

scholarly journals Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4107
Author(s):  
Akylas Stratigakos ◽  
Athanasios Bachoumis ◽  
Vasiliki Vita ◽  
Elias Zafiropoulos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Singular Spectrum Analysis ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Short Term ◽  
Singular Spectrum

Short-term electricity load forecasting is key to the safe, reliable, and economical operation of power systems. An important challenge that arises with high-frequency load series, e.g., hourly load, is how to deal with the complex seasonal patterns that are present. Standard approaches suggest either removing seasonality prior to modeling or applying time series decomposition. This work proposes a hybrid approach that combines Singular Spectrum Analysis (SSA)-based decomposition and Artificial Neural Networks (ANNs) for day-ahead hourly load forecasting. First, the trajectory matrix of the time series is constructed and decomposed into trend, oscillating, and noise components. Next, the extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or a Long Short-Term Memory (LSTM) predictive layer. The model is further extended to include exogenous features, e.g., weather forecasts, transformed via parallel dense layers. The predictive performance is evaluated on two real-world datasets, controlling for the effect of exogenous features on predictive accuracy. The results showcase that the decomposition step improves the relative performance for ANN models, with the combination of LSTM and SAA providing the best overall performance.

Peak Load Forecasting Using Long-Short Term Memory : Case Study of Jawa-Madura-Bali System

2021 ◽  
Author(s):  
Anindita Satria Surya ◽  
Musa Partahi Marbun ◽  
K.G.H. Mangunkusumo ◽  
Muhammad Ridwan
Keyword(s):  
Short Term Memory ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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