Data on pump efficiency optimization and social return of a solar-powered water pump in agricultural applications

This dataset presents experimental and socio-economic data used to evaluate and optimize the performance of a photovoltaic (PV) solar-powered water pumping system installed in agricultural areas of northern Thailand. The research hypothesis assumed that solar irradiance, PV tilt angle, and panel temperature (or ambient temperature) jointly influence pump efficiency in a nonlinear manner and that the system performance can be optimized using the Response Surface Methodology (RSM). The data include measurements of pump efficiency under 15 experimental conditions based on a Box–Behnken design, covering solar irradiance (300–1000 W/m²), PV tilt angle (10–35°), and temperature (30–60 °C). These measurements were collected under actual field conditions in coffee- and crop-growing areas in Chiang Mai and Lampang Provinces.

The dataset shows that pump efficiencies ranged from 51.4–85.2% depending on operating conditions. The highest efficiency (≈85%) occurred at moderate irradiance (≈800 W/m²), a tilt angle of 20–25°, and temperatures around 40–45 °C. Extremely high temperatures (>45 °C) reduced efficiency due to thermal losses in the PV modules. Statistical analysis confirmed that solar irradiance was the most influential factor, followed by panel temperature and tilt angle. The developed quadratic regression model provided an excellent fit (R² > 0.99), and diagnostic plots confirmed model adequacy, normality of residuals, and strong agreement between predicted and experimental values. Response surface plots and interaction contours illustrate how combinations of the three variables affect performance and identify the optimal region.

Beyond technical performance, the dataset also includes economic and social impact data derived from Social Return on Investment (SROI) analysis. Costs, energy use, and stakeholder feedback were collected through field surveys, interviews, and operational records. The SROI analysis compares the solar PV pumping system with a conventional groundwater pumping system and quantifies social and economic benefits for farmers and local communities over a three-year period. The results indicate consistently increasing social value, lower energy costs, reduced fuel dependency, and improved long-term sustainability.

Together, the dataset provides comprehensive technical, environmental, and socio-economic information supporting the optimization, performance modeling, and feasibility assessment of PV water pumping systems in rural agricultural regions. These data can be used for system design, RSM-based optimization, validation of numerical models, and sustainability assessments in similar climatic or agricultural contexts.