• Sanjeev Kumar Sukalikar
• Dr S.R. Awasthi

In order to manage the growing demand of electrical power, with increase in load, Smart Power Grids are required for proper distribution of electrical load. Here managing the demand is not by straight switching because of random power requirements. Considering these facts, the present work is focused on developing technique for predicting the renewable energy (solar) by using genetic algorithm. Here GASPP (Genetic Algorithm Based Solar Power prediction) will utilize Teacher Learning Based Optimization (TLBO) algorithm, where two phase learning is involved. Here proper set of environmental variables are collected and their effect on solar power generation is evaluated by TLBO. Outcome of this GASPP model is the ratio of environmental variables which directly affect power generation. The developed program was applied on Raipur city in Chhattisgarh state of India and result shows that GASPP has improved the accuracy of power prediction values taken from Indian railway top roof solar installation.

Electric power Grid, Renewable resources -Solar.

"Genetic Algorithm Based Solar Power Prediction Using Environmental Features ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.24-34, June 2019, Available :http://www.jetir.org/papers/JETIR1906I34.pdf

"Genetic Algorithm Based Solar Power Prediction Using Environmental Features ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp24-34, June 2019, Available at : http://www.jetir.org/papers/JETIR1906I34.pdf