DocumentCode
2298702
Title
Notice of Retraction
Marginal price forecasting based on KNN-IA algorithm
Author
Fang Zhou ; Min Wu
Author_Institution
Dept. of Math., Xianning Univ., Xianning, China
Volume
1
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
372
Lastpage
376
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In electricity market, electricity price is one of the main concerns for both suppliers and consumers. However, uncertain factors which effect electricity prices make it difficult to forecast them. This paper proposes a time series forecasting model which integrates K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) algorithms. The KNN algorithm is used to search for similar time subseries (neighbors) in historical data and the IA is used to search for the optimal weights on these neighbors. The model is tested by using dataset of the electricity price in New York electricity markets. Compared with traditional ARIMA and Naive I model, our model can get more accurate forecasting results.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In electricity market, electricity price is one of the main concerns for both suppliers and consumers. However, uncertain factors which effect electricity prices make it difficult to forecast them. This paper proposes a time series forecasting model which integrates K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) algorithms. The KNN algorithm is used to search for similar time subseries (neighbors) in historical data and the IA is used to search for the optimal weights on these neighbors. The model is tested by using dataset of the electricity price in New York electricity markets. Compared with traditional ARIMA and Naive I model, our model can get more accurate forecasting results.
Keywords
neural nets; power engineering computing; power markets; pricing; time series; KNN-IA algorithm; artificial neural network; electricity market; electricity price; k-nearest neighbor algorithm; marginal price forecasting; optimal weights; time series forecasting model; Artificial neural networks; Autoregressive processes; Electricity; Forecasting; Nearest neighbor searches; Prediction algorithms; Predictive models; K-Nearest Neighbors; artificial neural network; electricity price forecasting; weight;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5583817
Filename
5583817
Link To Document