Energy Supply Management Using the Solar Energy Prediction in Smart Grid

2016 ◽  
Vol 11 (5) ◽  
pp. 486
Author(s):  
Abdelilah Kahaji ◽  
Rachid Alaoui ◽  
Sadik Farhat ◽  
Lahoussine Bouhouch
Keyword(s):  
Solar Energy ◽  
Smart Grid ◽  
Energy Supply ◽  
Supply Management ◽  
Energy Prediction

Energy Prediction Using Data Analytics in Smart Grid

2018 ◽  
Vol 06 (06) ◽  
pp. 110-115
Author(s):  
Panchami Anil ◽  
Anas P V ◽  
Naseef Kuruvakkottil ◽  
Anusha K V ◽  
Balagopal N
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Energy Prediction ◽  
Using Data

scholarly journals Photovoltaic solar energy in the economic optimisation of energy supply and conversion

2018 ◽  
Vol 12 (11) ◽  
pp. 1263-1268 ◽  
Author(s):  
Danielle Delgado ◽  
Monica Carvalho ◽  
Luiz Moreira Coelho Junior ◽  
Raphael Abrahão ◽  
Ricardo Chacartegui
Keyword(s):  
Solar Energy ◽  
Energy Supply ◽  
Photovoltaic Solar Energy

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

Deep Learning based Models for Solar Energy Prediction

2021 ◽  
Vol 6 (1) ◽  
pp. 349-355
Author(s):  
Imane Jebli ◽  
Fatima-Zahra Belouadha ◽  
Mohammed Issam Kabbaj ◽  
Amine Tilioua
Keyword(s):  
Deep Learning ◽  
Solar Energy ◽  
Energy Prediction

Renewable Energy Sources Development in Rural Areas of African Countries

2019 ◽  
pp. 154-167 ◽  
Author(s):  
Liudmila V. Nefedova ◽  
Alexander Alexsvitch Solovyev ◽  
Olena Popova
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Rural Areas ◽  
Energy Supply ◽  
Sustainable Energy ◽  
Renewable Energy Sources ◽  
Energy Development ◽  
Energy Sources ◽  
African Countries ◽  
Decentralized Energy

The prospects of increasing access to electricity for the population of rural areas of Africa are considered. The main international funds and organizations aimed at sustainable energy development in Africa are described. An analysis of the state and possible options for using renewable energy sources for this purpose in decentralized energy supply through the creation of mini-grids or stand-alone systems is given. The risks by developing renewable energy sources in rural areas and modern mechanisms for financing in solar energy are presented.

Renewable Energy Sources Development in Rural Areas of African Countries

2021 ◽  
pp. 1691-1704
Author(s):  
Liudmila V. Nefedova ◽  
Alexander Alexsvitch Solovyev ◽  
Olena Popova
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Rural Areas ◽  
Energy Supply ◽  
Sustainable Energy ◽  
Renewable Energy Sources ◽  
Energy Development ◽  
Energy Sources ◽  
African Countries ◽  
Decentralized Energy

The prospects of increasing access to electricity for the population of rural areas of Africa are considered. The main international funds and organizations aimed at sustainable energy development in Africa are described. An analysis of the state and possible options for using renewable energy sources for this purpose in decentralized energy supply through the creation of mini-grids or stand-alone systems is given. The risks by developing renewable energy sources in rural areas and modern mechanisms for financing in solar energy are presented.

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