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.本数据集包含用于评估与优化泰国北部农业区域安装的光伏（PV）太阳能抽水系统性能的实验数据与社会经济数据。本研究的假设为：太阳辐照度、PV倾斜角与光伏组件温度（或环境温度）会以非线性方式共同影响水泵效率，且可通过响应面法（Response Surface Methodology, RSM）优化系统性能。本数据集包含基于Box-Behnken试验设计的15种实验条件下的水泵效率测量数据，涵盖太阳辐照度（300~1000 W/m²）、PV倾斜角（10~35°）以及温度（30~60 ℃）。这些测量数据采集自清迈府与南邦府的咖啡与农作物种植区域的实际田间环境。 本数据集显示，水泵效率随运行工况变化，区间为51.4%~85.2%。最高效率（约85%）出现在中等辐照度（约800 W/m²）、倾斜角20~25°以及温度40~45 ℃左右的工况下。当温度极高（>45 ℃）时，光伏组件的热损耗会导致效率下降。统计分析表明，太阳辐照度是影响效率最显著的因素，其次为光伏组件温度与倾斜角。所构建的二次回归模型拟合效果极佳（决定系数R²>0.99），诊断图验证了模型的适用性、残差的正态性，以及预测值与实验值之间的高度一致性。响应面图与交互等高线图阐明了三个变量的组合如何影响系统性能，并确定了最优工况区间。 除技术性能数据外，本数据集还包含通过社会投资回报（Social Return on Investment, SROI）分析得到的经济与社会影响数据。研究通过田间调研、访谈与运行记录收集了成本、能源使用情况以及利益相关方反馈数据。该社会投资回报分析将太阳能PV抽水系统与传统地下水抽水系统进行对比，并量化了三年内为农户与当地社区带来的社会与经济效益。分析结果显示，社会价值持续提升、能源成本降低、燃料依赖度下降，且长期可持续性得到改善。 综上，本数据集提供了全面的技术、环境与社会经济信息，可为农村农业区域的PV抽水系统优化、性能建模与可行性评估提供支撑。在气候或农业环境相似的场景中，这些数据可用于系统设计、基于RSM的优化、数值模型验证以及可持续性评估。