Artificial neural networks in power systems. Part 1: General introduction to neural computing

1997 ◽  
Vol 11 (3) ◽  
pp. 129-134 ◽  
Author(s):  
R. Aggarwal ◽  
Y. Song
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Neural Computing ◽  
General Introduction ◽  
Artificial Neural

Applications of Artificial Neural Networks in the Context of Power Systems

2020 ◽  
pp. 345-373
Author(s):  
Jan-Hendrik Menke ◽  
Marcel Dipp ◽  
Zheng Liu ◽  
Chenjie Ma ◽  
Florian Schäfer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Artificial Neural

scholarly journals Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural Networks

2018 ◽  
Vol 8 (1) ◽  
pp. 497 ◽  
Author(s):  
Mohammad H. Alomari ◽  
Jehad Adeeb ◽  
Ola Younis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Systems ◽  
Output Power ◽  
Solar Irradiance ◽  
Machine Learning Algorithms ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Intensive Training ◽  
Artificial Neural

In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.

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