Genetic Algorithm Based Optimum Solar Power Prediction by Environmental Features for Indian Railway Stations
Sanjeev Kumar Sukalikar1, S.R. Awasthi2
1Sanjeev Kumar Sukalikar, Ph.D. Scholar, Department of Electrical Engineering, Rabindranath Tagore University, Bhopal.
2Dr S.R. Awasthi, Supervisor, Department of Electrical Engineering, Rabindranath Tagore University, Bhopal.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 549-556 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7278118419/2019©BEIESP | DOI: 10.35940/ijrte.D7278.118419

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Abstract: Electric Power requirement is increasing with every new day. Bulk of electric power in India is consumed by Indian railways, hence green energy resources are required to reduce this load. Out of many available green energy resources solar energy is playing an important role. Hence desired optimum power output from solar plant is always of area of research. This work focuses on estimation of solar plant output with the affecting environmental variables by using genetic algorithm. Genetic algorithm predicts the ratio of environmental variables that directly increase or decrease the solar power output. Work will determine this ratio by using modified butterfly particle swarm optimization algorithm and Teacher Learning Based Optimization. Here it was obtained that proposed TLBO model is good for estimating monthly power prediction while MBAPSO estimate accurate values for daily power prediction. Proposed model experiment was done on dataset of Raipur city in Chhattisgarh state of India and the results show reduction in MAE, RMSE and improvement in Correlation Coefficient thereby accuracy in power prediction when compared with ground truth values taken from Indian railway top roof solar installation.
Keywords: Electric power Grid, Renewable Resources-Solar.
Scope of the Article: Cloud Resources Utilization in IoT.