Designing solar heating systems is important. This study dissolved boehmite nanofluid (ALOOH) in water and used it in a solar heater with a flat plate collector. The solar flat plate collector outlet temperature was higher in this case. The outlet fluid temperature was predicted using an artificial neural network and support vector regression methods. Data collection was conducted for 18 days in July and August in Kermanshah city (47.7N, 34.23 E). The nanofluid concentration was set to 0.2 wt%. Two series of input were considered:• Operator fluid type, flow, time, environment, and input fluid temperatures,•Operator fluid type, flow, time, environment, input and outlet fluid temperatures, the flat plate collector temperature, the temperature of the lower point of the collector, and the temperature of the glass shield.The results indicated that the ANN method was more powerful in predicting the solar heater outlet temperature. Increasing the input factors, the accuracy of the ANN method was also enhanced in a way that its accuracy was 99.7619 for the first series, which increased to 99.8709 for the second one. A comparison of the ANN results with those of the SVR method showed the superiority of the ANN method.
