Generating Artificial Weather Data Sequences for Solar Distillation Numerical Simulations

*Bao The Nguyen*

#### **Abstract**

According to the natural geographical distribution, developing countries are concentrated in tropical climates, where radiation is abundant. So the use of solar energy is a sustainable solution for developing countries. However, daily or hourly measured solar irradiance data for designing or running simulations for solar systems in these countries is not always available. Therefore, this chapter presents a model to calculate the daily and hourly radiation data from the monthly average daily radiation. First, the chapter describes the application of Aguiar's model to the calculation of daily radiation from average daily radiation data. Next, the chapter presents an improved Graham model to generate hourly radiation data series from monthly radiation. The above two models were used to generate daily and hourly radiation data series for Ho Chi Minh City and Da Nang, two cities representing two different tropical climates. The generated data series are tested by comparing the statistical parameters with the measured data series. Statistical comparison results show that the generated data series have acceptable statistical accuracy. After that, the generated radiation data series continue to be used to run the simulation program to calculate the solar water distillation system and compare the simulation results with the radiation data. Measuring radiation. The comparison results once again confirm the accuracy of the solar irradiance data generation model in this study. Especially, the model to generate the sequences of hourly solar radiation values proposed in this study is much simpler in comparison to the original model of Graham. In addition, a model to generate hourly ambient tempearure date from monthly average daily ambient temperature is also presented and tested. Then, both generated hourly solar radiation and ambient temperature sequences are used to run a solar dsitillation simulation program to give the outputs as monthly average daily distillate productivities. Finally, the outputs of the simulation program running with the generated solar radiation and ambient temperature data are compared with those running with measured data. The errors of predicted monthly average daily distillate productivities between measured and generated weather data for all cases are acceptably low. Therefore, it can be concluded that the model to generate artificial weather data sequences in this study can be used to run any solar distillation simulation programs with acceptable accuracy.

**Keywords:** solar stills, solar distillation numerical simulations, monthly average daily solar radiation, hourly solar radiation values, monthly average daily temperature, hourly monthly-average ambient temperature, Markov Transition Matrix
