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

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.

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongze Li ◽  
Liuyang Cui ◽  
Sen Guo

Short-term power load forecasting is one of the most important issues in the economic and reliable operation of electricity power system. Taking the characteristics of randomness, tendency, and periodicity of short-term power load into account, a new method (SSA-AR model) which combines the univariate singular spectrum analysis and autoregressive model is proposed. Firstly, the singular spectrum analysis (SSA) is employed to decompose and reconstruct the original power load series. Secondly, the autoregressive (AR) model is used to forecast based on the reconstructed power load series. The employed data is the hourly power load series of the Mid-Atlantic region in PJM electricity market. Empirical analysis result shows that, compared with the single autoregressive model (AR), SSA-based linear recurrent method (SSA-LRF), and BPNN (backpropagation neural network) model, the proposed SSA-AR method has a better performance in terms of short-term power load forecasting.


MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 101015
Author(s):  
Winita Sulandari ◽  
S. Subanar ◽  
Muhammad Hisyam Lee ◽  
Paulo Canas Rodrigues

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunyan Shuai ◽  
Zhengyang Pan ◽  
Lun Gao ◽  
HongWu Zuo

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.


2020 ◽  
Vol 216 ◽  
pp. 01016
Author(s):  
Nikolay Zubov ◽  
Misrikhan Misrikhanov ◽  
Vladimir Ryabchenko ◽  
Andrey Shuntov

The results of forecasting the failure rate (failure frequency) of overhead lines (OHL) 500 kV, presented in the form of a time series with signs of chaos, are presented. Predictive estimates are obtained using methods of singular spectrum analysis, neural and fuzzy neural networks. As an object of singular spectrum analysis, a delay matrix is used, which is formed on the basis of the time series of the failure rate. The prediction was carried out by means of one-step transformations of the initial data. For prediction using a neural network, a direct signal transmission network is used, trained by the backpropagation method. In order to achieve the minimum mean squared error, the training sample contained the maximum possible history. To predict the failure rate by the method of fuzzy neural networks, the Wang-Mendel network was chosen. In all prediction cases, within the framework of one prediction year, 10 thousand "training - prediction" cycles were performed, which ensured the stationarity property of the histograms of the failure rate distributions.


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