Load forecasting techniques for networks with distributed generation (DG) sources

2016 ◽  
Author(s):  
Hu Xiaojing ◽  
Wei Lei ◽  
Jiang Ning ◽  
Jia Honggang ◽  
Yan Na
Keyword(s):  
Distributed Generation ◽  
Load Forecasting ◽  
Forecasting Techniques

A Review on Energy-Efficient Smart Home Load Forecasting Techniques

2021 ◽  
Author(s):  
Zahraa A. Jaaz ◽  
Mohd Ezanee Rusli ◽  
Nur Azzamuddin Rahmat ◽  
Inteasar Yaseen Khudhair ◽  
Israa Al Barazanchi ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Smart Home ◽  
Load Forecasting ◽  
Forecasting Techniques

Comparison of very short-term load forecasting techniques

1996 ◽  
Vol 11 (2) ◽  
pp. 877-882 ◽  
Author(s):  
K. Liu ◽  
S. Subbarayan ◽  
R.R. Shoults ◽  
M.T. Manry ◽  
C. Kwan ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Forecasting Techniques

A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

2015 ◽  
Vol 170 ◽  
pp. 448-465 ◽  
Author(s):  
M. Fagiani ◽  
S. Squartini ◽  
L. Gabrielli ◽  
S. Spinsante ◽  
F. Piazza
Keyword(s):  
Natural Gas ◽  
Load Forecasting ◽  
Forecasting Techniques

Daily Load Forecasting Based on Combination Forecasting Techniques

2011 ◽  
Vol 201-203 ◽  
pp. 2685-2689
Author(s):  
Chong Gao ◽  
Hai Jie Ma ◽  
Pei Na Gao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Ar Model ◽  
Neural Network Training ◽  
Network Training ◽  
Artificial Neural ◽  
Daily Load ◽  
Forecasting Techniques

To improve the accuracy of load forecasting is the focus of the load forecasting. As the daily load by various environmental factors and periodical, this makes the load time series of changes occurring during non-stationary random process. The key of improving the accuracy of artificial neural network training is to select effective training sample. This paper based on the time series forecasting techniques’ random time series autocorrelation function to select the neural network training samples. The method of modeling is more objective. By example, the comparison with autoregressive (AR) Model predictions and BP Artificial Neural Network (ANN) predicted results through error analysis and confirmed the proposed scheme good performance.

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