scholarly journals Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks

Energies ◽  
2014 ◽  
Vol 7 (5) ◽  
pp. 2938-2960 ◽  
Author(s):  
Christopher Bennett ◽  
Rodney Stewart ◽  
Junwei Lu
Keyword(s):  
Neural Network ◽  
Low Voltage ◽  
Distribution Networks ◽  
Voltage Distribution ◽  
Short Term ◽  
Exogenous Variables ◽  
Load Forecast ◽  
Forecast Models

A Comparative Analysis of Neural Network Based Short Term Load Forecast Models for Anomalous Days Load Prediction

2014 ◽  
Vol 9 (7) ◽  
Author(s):  
Muhammad Qamar Raza ◽  
Zuhairi Baharudin ◽  
Badar-Ul-Islam Badar-Ul-Islam ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Load Prediction ◽  
Short Term ◽  
Load Forecast ◽  
Forecast Models

scholarly journals COMPREHENSIVE METHOD FOR EVALUATION OF MEDIUM- AND LOW-VOLTAGE DISTRIBUTION NETWORK OPERATING STATE

2020 ◽  
Vol 2020 (6) ◽  
pp. 47-56
Author(s):  
Shiwei Su ◽  
◽  
Yiran You ◽  
Yu Zou ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Evaluation System ◽  
Low Voltage ◽  
Accurate Determination ◽  
Distribution Network ◽  
Distribution Networks ◽  
Entropy Method ◽  
Weak Links ◽  
Voltage Distribution ◽  
Self Organizing

With the development of intelligent distribution networks and access to distributed energy, the solving the problem of timely and accurate determination of the operating state of the distribution network is an urgent task. Based on an improved analysis of the principle components of the network and statements of a self-organizing neural network, this article proposes the method to evaluate the operating state of medium- and low-voltage distribution networks. At the first step, the system of evaluating indices of the network is formed by advanced component analysis. The evaluation system is grounded on four aspects, including safety, reliability, quality and economy. Next, the self-organizing neural network is used to identify and clean up the data regarding the operating state of the distribution network. At the next step, the indicators are modeled at all levels; the entropy method is applied to calculate the total weight of all indicators. Then the value of each indicator is found and the weak links of the distribution network are determined. At the final stage, the comprehensive assessment of the real operation of the distribution network in Guangxi province is carried out. As shown, the method can effectively reduce the effect of abnormal data and subjectivity factor on the results of evaluating the state of the distribution network. That confirms the feasibility and practicability of the proposed method. References 22, figures 6, tables 6.

Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems

2016 ◽  
Vol 31 (1) ◽  
pp. 72-81 ◽  
Author(s):  
Ni Ding ◽  
Clementine Benoit ◽  
Guillaume Foggia ◽  
Yvon Besanger ◽  
Frederic Wurtz
Keyword(s):  
Neural Network ◽  
Distribution Systems ◽  
Model Design ◽  
Short Term ◽  
Load Forecast

Single-Phase Generation Headroom in Low-Voltage Distribution Networks Under Reduced Circuit Characterization

2015 ◽  
Vol 30 (2) ◽  
pp. 1006-1011 ◽  
Author(s):  
Pedro M. S. Carvalho ◽  
Luis A. F. M. Ferreira ◽  
Joao J. E. Santana
Keyword(s):  
Single Phase ◽  
Low Voltage ◽  
Distribution Networks ◽  
Voltage Distribution
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