Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a nove...

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Bibliographic Details
Published inEnergies (Basel) Vol. 12; no. 5; p. 916
Main Authors Fan, Guo-Feng, Guo, Yan-Hui, Zheng, Jia-Mei, Hong, Wei-Chiang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2019
Subjects
Accuracy
Algorithms
Big Data
Classification
comparative analysis
Data points
Decision making
Distributed processing
Economic forecasting
Electric power systems
Electricity
Electricity consumption
Euclidean geometry
Forecasting
Genetic algorithms
Literature reviews
Load
Market prices
Neural networks
Parameterization
Power flow
Short term
short-term load forecasting
Time series
weighted k-nearest neighbor (W-K-NN) algorithm
China
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Summary:In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12050916