Optimal Location to Use Solar Energy in an Urban Situation

This study conducted in Lima, Peru, a combination of spatial deci-sion making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location. The test state's Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province's study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable. The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.

Automated summary

This study conducted in Lima, Peru utilized a combination of spatial decision-making system and machine learning to identify potential sites for solar power plant construction. Data on solar radiation, precipitation, temperature, and altitude were collected through sundial measurements. The Gene Expression Programming (GEP) and Artificial Neural Networks (ANN) were used to predict the locations, with GEP proving to be the most suitable network with a test state's Nash-Sutcliffe efficiency (NSE) of 0.90 and root-mean-square error (RMSE) of 0.04. The final map based on the GEP model showed that 9.2% of the study area is suitable for construction, while the ANN model indicated only 1.7% suitability.