A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price
In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forec...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/249208 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562927555575808 |
|---|---|
| author | Zhilong Wang Feng Liu Jie Wu Jianzhou Wang |
| author_facet | Zhilong Wang Feng Liu Jie Wu Jianzhou Wang |
| author_sort | Zhilong Wang |
| collection | DOAJ |
| description | In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO), the backpropagation artificial neural network (BPANN), and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN) method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations. |
| format | Article |
| id | doaj-art-15fcc6b0e589488b92dfbe8261d40461 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-15fcc6b0e589488b92dfbe8261d404612025-02-03T01:21:24ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/249208249208A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity PriceZhilong Wang0Feng Liu1Jie Wu2Jianzhou Wang3Department of Basic Courses, Lanzhou Institute of Technology, Lanzhou 730050, ChinaSchool of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaSchool of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaSchool of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaIn the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO), the backpropagation artificial neural network (BPANN), and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN) method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.http://dx.doi.org/10.1155/2014/249208 |
| spellingShingle | Zhilong Wang Feng Liu Jie Wu Jianzhou Wang A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price Abstract and Applied Analysis |
| title | A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price |
| title_full | A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price |
| title_fullStr | A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price |
| title_full_unstemmed | A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price |
| title_short | A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price |
| title_sort | hybrid forecasting model based on bivariate division and a backpropagation artificial neural network optimized by chaos particle swarm optimization for day ahead electricity price |
| url | http://dx.doi.org/10.1155/2014/249208 |
| work_keys_str_mv | AT zhilongwang ahybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT fengliu ahybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT jiewu ahybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT jianzhouwang ahybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT zhilongwang hybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT fengliu hybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT jiewu hybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice AT jianzhouwang hybridforecastingmodelbasedonbivariatedivisionandabackpropagationartificialneuralnetworkoptimizedbychaosparticleswarmoptimizationfordayaheadelectricityprice |