The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia
Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electrici...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/172306 |
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| author | Jianzhou Wang Ling Xiao Jun Shi |
| author_facet | Jianzhou Wang Ling Xiao Jun Shi |
| author_sort | Jianzhou Wang |
| collection | DOAJ |
| description | Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models. |
| format | Article |
| id | doaj-art-95bba71f4be34694ab6a767ddcb72f34 |
| 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-95bba71f4be34694ab6a767ddcb72f342025-02-03T00:59:50ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/172306172306The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South AustraliaJianzhou Wang0Ling Xiao1Jun Shi2School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, ChinaSchool of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaElectricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.http://dx.doi.org/10.1155/2014/172306 |
| spellingShingle | Jianzhou Wang Ling Xiao Jun Shi The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia Abstract and Applied Analysis |
| title | The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia |
| title_full | The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia |
| title_fullStr | The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia |
| title_full_unstemmed | The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia |
| title_short | The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia |
| title_sort | combination forecasting of electricity price based on price spikes processing a case study in south australia |
| url | http://dx.doi.org/10.1155/2014/172306 |
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