A hybrid model for daily peak load power forecasting based on SAMBA and neural network

2014 ◽  
Vol 27 (2) ◽  
pp. 913-920 ◽  
Author(s):  
Masoud Bakhshi Germi ◽  
Mohammad Mirjavadi ◽  
Aghil Seyed Sadeghi Namin ◽  
Aliasghar Baziar
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Peak Load ◽  
Load Power ◽  
Power Forecasting

scholarly journals A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

2016 ◽  
Vol 70 ◽  
pp. 10002 ◽  
Author(s):  
Jie Zeng ◽  
Chi Zhang ◽  
Ning Xie ◽  
Ping Yang ◽  
Chenyu Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Hybrid Model ◽  
Bp Neural Network ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Power Forecasting

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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