Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone

The development of energy-efficient buildings by considering early-stage design parameters can help reduce buildings' energy consumption. Machine learning tools are getting popular for forecasting the energy demand of buildings, which play a vital role in improving building energy efficiency. In this research, multi-expression and genetic expression programming were utilized to anticipate the energy consumption of PCM-integrated buildings by taking early-stage design parameters into consideration. The prediction models were developed using the data generated by energy simulations for the PCM-integrated building in eight cities within a tropical savanna climate. The statical parameters were used to evaluate and externally validate the proposed prediction model. The statistical evaluation reveals that the genetic expression programming-based predictive model gave more accurate energy consumption predictions for PCM-integrated buildings than multi-expression programming. The performance indices of the statistically analyzed gene expression programming-based prediction model (GEP7) showed excellent values: correlation coefficient (R) = 0.961, performance index (ρ) = 0.169, and Nash-Sutcliffe efficiency (NSE) = 0.108. Thereafter, the sensitivity and parametric analyses were performed. It was unearthed that the roof solar absorptance, window visible transmittance, wall solar absorptance, and the melting temperature of PCM were the influential early-stage design parameters for PCM-integrated buildings. In conclusion, the gene-expression programming-based predictive model can be utilized to predict the influence of early-stage design parameters on the energy consumption of PCM-integrated buildings.