Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models

2015 ◽  
Vol 64 ◽  
pp. 340-346 ◽  
Author(s):  
G. Sudheer ◽  
A. Suseelatha
Keyword(s):  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals A wavelet‐nearest neighbor model for short‐term load forecasting

2014 ◽  
Vol 3 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Gopinathan Sudheer ◽  
Annamareddy Suseelatha
Keyword(s):  
Nearest Neighbor ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Short-term Load Forecasting for Microgrids Based on Discrete Wavelet Transform and BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 738-742 ◽  
Author(s):  
Chong Gao ◽  
sheng Huang ◽  
Hai-feng Wang
Keyword(s):  
Wavelet Transform ◽  
Load Forecasting ◽  
Discrete Wavelet ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Micro Grid ◽  
Grid Load ◽  
The Time Domain ◽  
National Economies ◽  
Better Than

Electricity is of great vital and indispensable to national economies. A new short-term load forecasting for micro grid is proposed in this paper. After comparing and analyzing all load characteristic in the time domain and frequency domain, we apply wavelet transform to decompose the load signal. After that, the training set and text set are selected in consideration of the effects generated by the temperature and day type. At length, BP natural network is employed you forecast the micro grid load. The final result proves that the forecasting precision of the method we propose is obviously better than the traditional ones. What’s more, our method has Strong adaptability and good generalization ability.

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

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 916 ◽  
Author(s):  
Guo-Feng Fan ◽  
Yan-Hui Guo ◽  
Jia-Mei Zheng ◽  
Wei-Chiang Hong
Keyword(s):  
Nearest Neighbor ◽  
Load Forecasting ◽  
Forecasting Model ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Nearest Neighbor Algorithm ◽  
Comparison Results ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
K Nearest Neighbor Algorithm

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.

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