Power load forecasting based on long short term memory-singular spectrum analysis

2021 ◽  
Author(s):  
Neeraj ◽  
Jimson Mathew ◽  
Ranjan Kumar Behera
Keyword(s):  
Spectrum Analysis ◽  
Short Term Memory ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Singular Spectrum ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory

scholarly journals A Hybrid Short-Term Power Load Forecasting Model Based on the Singular Spectrum Analysis and Autoregressive Model

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongze Li ◽  
Liuyang Cui ◽  
Sen Guo
Keyword(s):  
Spectrum Analysis ◽  
Electricity Market ◽  
Autoregressive Model ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Ar Model ◽  
Short Term ◽  
Singular Spectrum ◽  
Power Load ◽  
Power Load Forecasting

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.

scholarly journals Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Hla U May Marma ◽  
M. Tariq Iqbal ◽  
Christopher Thomas Seary
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour.  In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

Short-term power load forecasting using integrated methods based on long short-term memory

2020 ◽  
Vol 63 (4) ◽  
pp. 614-624
Author(s):  
WenJie Zhang ◽  
Jian Qin ◽  
Feng Mei ◽  
JunJie Fu ◽  
Bo Dai ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Integrated Methods ◽  
Long Short Term Memory
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