Integrated Data-Driven and Interaction-Based Evaluation of Hybrid PV–Wind Systems for Sustainable Urban Park Lighting Design

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Chinese explanation / 中文解读

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Original abstract

The growing demand for sustainable urban infrastructure requires reliable and energy-efficient lighting solutions, particularly for urban parks characterized by dynamic energy consumption patterns. This study proposes an integrated analytical framework combining deterministic energy system modeling, a novel interaction-oriented metric, and machine learning techniques to evaluate hybrid photovoltaic (PV)–wind systems for urban park lighting applications. Multi-year hourly meteorological and lighting-demand data from millet gardens in Balıkesir and Çanakkale, Türkiye, were analyzed. A Hybrid Energy Synergy Index (HESI) was introduced to quantify the degree of concurrent contribution and coordination between PV and wind resources relative to lighting demand. The results show substantial demand variability, with an average daily demand of approximately 1609 kWh and peak values exceeding 25,600 kWh. Photovoltaic generation dominated total renewable energy production (92.95%), whereas wind energy contributed 7.05%. The average HESI value (≈0.113) indicated weak source coordination, accompanied by persistent energy deficits that occasionally exceeded −20,000 kWh. Reliability analysis revealed severe system inadequacy, with a reliability rate of only 0.000547 (0.055%). Within the evaluated design space, the highest-performing configuration consisted of 49 PV panels and 19 wind turbines; however, reliability improvements remained limited, indicating that capacity expansion alone is insufficient to ensure satisfactory performance. Machine learning models achieved high predictive accuracy for HESI forecasting (R2 = 0.9902; MAE = 0.0027), while explainable artificial intelligence identified solar radiation and wind speed as the dominant environmental drivers. The results highlight the importance of integrating renewable energy capacity planning with energy storage support, improved source coordination, and adaptive energy management strategies to enhance the reliability of sustainable urban lighting systems.

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