Dataset on the Performance of a Photovoltaic Solar Water Pump in Coffee Plantations Using Response Surface Methodology (RSM)
Description
This dataset was generated to investigate how key environmental and operational factors influence the performance of a photovoltaic (PV) solar-powered water pumping system installed in a coffee plantation in Chiang Mai Province, Thailand. The research hypothesis posits that the combined effects of solar irradiance, panel inclination, and panel surface temperature significantly influence pumping efficiency, and that an optimal operating point can be identified using a structured statistical approach. The data were collected through 15 experimental runs designed using Response Surface Methodology (RSM), which enabled the systematic variation of three independent variables: solar irradiance (300–900 W/m²), panel inclination (15–35°), and panel surface temperature (30–60 °C). For each condition, the pumping efficiency (%) was measured, and both raw and processed data—including residual diagnostics, ANOVA outputs, regression coefficients, and response surface parameters—are provided. These elements allow users to fully reproduce the experimental workflow, model-building process, and optimization procedure. The dataset shows that solar irradiance is the most influential factor affecting pumping efficiency, followed by panel surface temperature and panel inclination. Efficiency generally increases with higher irradiance and moderate tilt angles, while excessively high panel temperatures lead to performance losses due to reduced photovoltaic electrical output. Statistical analyses confirm a highly reliable quadratic regression model (R² = 0.9972), with strong agreement between adjusted and predicted R², indicating excellent model adequacy and predictive capability. Visual outputs—including perturbation plots, 3D surface plots, and contour plots—illustrate the main and interaction effects of each variable. These plots reveal a clear nonlinear behavior in system performance, with all three factors exhibiting curvature consistent with the quadratic model. The optimal operating conditions were identified at a solar irradiance of 600 W/m², a panel inclination of 25°, and a panel temperature of 45 °C, corresponding to a predicted pumping efficiency of 76.3–77.0%. This dataset can be interpreted as a validated reference model for the design and optimization of PV-powered water pumping systems operating in agricultural environments, particularly in highland regions with fluctuating solar and temperature conditions. Researchers may reuse the data to calibrate simulation tools, develop predictive models, compare alternative pump designs, or assess how environmental variability impacts renewable-energy-based irrigation performance. The combined inclusion of raw data, statistical modeling files, and visual analyses ensures transparency and reproducibility, offering a comprehensive resource for renewable energy and agricultural engineering research.
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Institutions
- University of Phayao