Design and Field Evaluation of a Low-Cost Micro-Meteorological Sensing and Machine-Learning Framework for Hourly Photovoltaic Power Estimation

This study presents the design and field evaluation of a low-cost micro-meteorological sensing and machine-learning framework for hourly photovoltaic (PV) power estimation. A 545 W PV module and 750 W grid-tie inverter were instrumented with a NodeMCU ESP8266-based acquisition system integrating local weather sensing and electrical power measurement. Five-minute field observations collected from 1 January to 3 September 2026 were aggregated to hourly resolution with gap-aware processing. Of 5899 candidate hourly timestamps, 5770 model-ready observations were retained (97.81%). Current-hour target power P(t) was estimated using contemporaneous sensing and preceding-hour power P(t − 1), with chronological 64/16/20 training/validation/test partitions and training-only preprocessing. The primary, ten-seed, three-input BPNN (illuminance, temperature, and preceding-hour power) achieved all-hours MAE 11.02 ± 0.88 W (mean ± sample SD), outperforming a three-input XGBoost (version 3.4.1) comparison (14.80 ± 0.20 W). Ten-seed paired MAE improvement was 3.79 ± 0.96 W, with a hierarchical moving-block bootstrap 95% interval of 2.90–4.63 W for the fixed test period. The full five-input BPNN was less accurate (12.52 ± 1.82 W); humidity and pressure did not improve average accuracy at this site. The original single-run BPNN MAE of 11.11 W was retained only as a separate historical comparison. The contribution was an auditable field-deployed estimation workflow rather than a new learning algorithm or validated on-device inference. The three-input BPNN was the recommended configuration. The five-input model remained a feature-ablation and full-feature baseline, not the principal performance claim. The estimated-power reference may complement direct PZEM-004T measurements; fault-detection accuracy, on-device inference, and generalization beyond the studied site were not evaluated. In the separate QC-cleaned ten-seed sensitivity analysis, all 1145 test hours fell within the observed training target-power range (0–349.92 W), yet MAE increased from 7.69 ± 0.88 W at ≤250 W to 29.24 ± 3.83 W above 250 W; reliability at high power remained limited.