scholarly journals A VMD–CISSA–LSSVM Based Electricity Load Forecasting Model

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 28
Author(s):  
Guijuan Wang ◽  
Xinheng Wang ◽  
Zuoxun Wang ◽  
Chunrui Ma ◽  
Zengxu Song
Keyword(s):  
Search Algorithm ◽  
Load Forecasting ◽  
Population Diversity ◽  
Support Vector ◽  
Forecasting Model ◽  
Initial Population ◽  
Search Range ◽  
Chaotic Mapping ◽  
Power Load ◽  
Search Capability

Accurate power load forecasting has an important impact on power systems. In order to improve the load forecasting accuracy, a new load forecasting model, VMD–CISSA–LSSVM, is proposed. The model combines the variational modal decomposition (VMD) data preprocessing method, the sparrow search algorithm (SSA) and the least squares support vector machine (LSSVM) model. A multi-strategy improved chaotic sparrow search algorithm (CISSA) is proposed to address the shortcomings of the SSA algorithm, which is prone to local optima and a slow convergence. The initial population is generated using an improved tent chaotic mapping to enhance the quality of the initial individuals and population diversity. Second, a random following strategy is used to optimize the position update process of the followers in the sparrow search algorithm, balancing the local exploitation performance and global search capability of the algorithm. Finally, the Levy flight strategy is used to expand the search range and local search capability. The results of the benchmark test function show that the CISSA algorithm has a better search accuracy and convergence performance. The volatility of the original load sequence is reduced by using VMD. The optimal parameters of the LSSVM are optimized by the CISSA. The simulation test results demonstrate that the VMD–CISSA–LSSVM model has the highest prediction accuracy and stabler prediction results.

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.

scholarly journals Research on Regional Short-Term Power Load Forecasting Model and Case Analysis

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1617
Author(s):  
Kang Qian ◽  
Xinyi Wang ◽  
Yue Yuan
Keyword(s):  
Meteorological Factors ◽  
Load Forecasting ◽  
Weighted Average ◽  
Support Vector ◽  
Forecasting Model ◽  
Electric Load ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Load Forecasting

Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.

A Combined Model Based on Cuckoo Search Algorithm for Electrical Load Forecasting

2015 ◽  
Vol 737 ◽  
pp. 278-282 ◽  
Author(s):  
En Hua Chang ◽  
Guan Nan Zhu ◽  
Jiong Wei Chen
Keyword(s):  
Search Algorithm ◽  
Load Forecasting ◽  
Energy Resource ◽  
Electric Power Industry ◽  
Forecasting Model ◽  
Power Prediction ◽  
Combined Model ◽  
Power Load ◽  
Electricity Power ◽  
Combined Forecasting

Abstract. Electricity power forecasting has been always playing a vital role in power system management and planning. Inaccurate prediction may lead to wastes of scarce energy resource, electricity shortages and even grid collapses. On the other hand, forecasting electricity power has proven to be a challenging task due to various unstable factors. Meanwhile, accurate power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general model for load forecasting that improves the performance in different cases. This paper proposes a combined forecasting model for electrical power prediction, and the particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and its results are promising.

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.

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.

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