Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Figure 5

A proposed framework of machine learning-assisted HTS process for target performance optimization. Independent variables are assigned as “ind.” Dependent variables are assigned as “dep.” {A_in} represents the original experimental database. {B_in} represents the generated independent variables as the inputs. {B_in(new)} represents the generated independent variables and their predicted dependent variables. {C_in} represents the new experimental database combining the original experimental database and the experimental validation results of the screened candidates.