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 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.

scholarly journals A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1579
Author(s):  
Xinheng Wang ◽  
Xiaojin Gao ◽  
Zuoxun Wang ◽  
Chunrui Ma ◽  
Zengxu Song
Keyword(s):  
Prediction Model ◽  
Spectrum Analysis ◽  
Search Algorithm ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Power Sector ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electricity Load

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

scholarly journals Forecasting for Ultra-Short-Term Electric Power Load Based on Integrated Artificial Neural Networks

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1063 ◽  
Author(s):  
Horng-Lin Shieh ◽  
Fu-Hsien Chen
Keyword(s):  
Energy Efficiency ◽  
Electric Power ◽  
Production Efficiency ◽  
Load Forecasting ◽  
Short Term ◽  
Inference System ◽  
Power Load ◽  
Power Load Forecasting ◽  
New Energy ◽  
Electric Power Load

Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.

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