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 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.

An Efficient Load Forecasting in Predictive Control Strategy Using Hybrid Neural Network

2019 ◽  
Vol 29 (01) ◽  
pp. 2050010 ◽  
Author(s):  
Shweta Sengar ◽  
Xiaodong Liu
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Cuckoo Search ◽  
Forecasting Model ◽  
Lévy Flight ◽  
Hybrid Neural Network ◽  
Levy Flight ◽  
Power Load ◽  
Power Load Forecasting

Load forecasting is a difficult task, because the load series is complex and exhibits several levels of seasonality. The load at a given hour is dependent not only on the load at the previous day, but also on the load at the same hour on the previous day and previous week, and because there are many important exogenous variables that must be considered. Most of the researches were simultaneously concentrated on the number of input variables to be considered for the load forecasting problem. In this paper, we concentrate on optimizing the load demand using forecasting of the weather conditions, water consumption, and electrical load. Here, the neural network (NN) power load forecasting model clubbed with Levy-flight from cuckoo search algorithm is proposed, i.e., called hybrid neural network (HNN), and named as LF-HNN, where the Levy-flight is used to automatically select the appropriate spread parameter value for the NN power load forecasting model. The results from the simulation work have demonstrated the value of the LF-HNN approach successfully selected the appropriate operating mode to achieve optimization of the overall energy efficiency of the system using all available energy resources.

scholarly journals A Power Load Forecasting Model Based on FA-CSSA-ELM

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zuoxun Wang ◽  
Xinheng Wang ◽  
Chunrui Ma ◽  
Zengxu Song
Keyword(s):  
Search Algorithm ◽  
Load Forecasting ◽  
Local Optimum ◽  
Chaotic Mapping ◽  
Forecasting Models ◽  
Power Resources ◽  
Power Load ◽  
Power Load Forecasting ◽  
Learning Machine ◽  
Grid Operation

Accurate and stable power load forecasting methods are essential for the rational allocation of power resources and grid operation. Due to the nonlinear nature of power loads, it is difficult for a single forecasting method to complete the forecasting task accurately and quickly. In this study, a new combined model for power loads forecasting is proposed. The initial weights and thresholds of the extreme learning machine (ELM) optimized by the chaotic sparrow search algorithm (CSSA) and improved by the firefly algorithm (FA) are used to improve the forecasting performance and achieve accurate forecasting. The early local optimum that exists in the sparrow algorithm is overcome by Tent chaotic mapping. A firefly perturbation strategy is used to improve the global optimization capability of the model. Real values from a power grid in Shandong are used to validate the prediction performance of the proposed FA-CSSA-ELM model. Experiments show that the proposed model produces more accurate forecasting results than other single forecasting models or combined forecasting models.

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