Negri Virginia, Tinarelli Roberto, Peretto Lorenzo, Mingotti Alessandro
Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi Alma Mater Studiorum, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy.
Sensors (Basel). 2025 Apr 14;25(8):2456. doi: 10.3390/s25082456.
Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed to addressing this issue, with research increasingly focused on sustainable practices. For instance, there is a growing trend in designing new buildings to be net-zero emitters, while older structures are being retrofitted for energy efficiency to achieve similar goals. To this purpose, the study aims to enhance the energy management capabilities of an educational building by implementing a smart infrastructure. Equipped with photovoltaic panels and a distributed measurement system, the building captures voltage and current data and calculates power. These electrical quantities are then forecasted through an AI-driven framework that manages the data. The paper details the AI model used, including its experimental validation. The results show that the system provides reliable forecasts of electrical parameters. The evaluation of the distributed measurement system and the collected data offers valuable insights, which support more informed actions for optimizing energy management and system performance. A key novelty of this study lies in the exploration of model generalization across measurement nodes. This approach is supported by the correlation analysis of data, which highlights the potential for accurate predictions in case of data gaps. Moreover, the ease of deployment and the practical application of the system were highlighted as key factors for scalability, allowing for potential adaptation in similar infrastructures.
近期，环境问题引发了各界对减轻人类影响的新解决方案的高度关注。电力系统领域也致力于解决这一问题，相关研究越来越多地聚焦于可持续发展实践。例如，设计新建筑以实现净零排放的趋势日益明显，同时对旧建筑进行节能改造以达到类似目标。为此，本研究旨在通过实施智能基础设施来提高教育建筑的能源管理能力。该建筑配备了光伏板和分布式测量系统，可采集电压和电流数据并计算功率。然后，通过一个管理数据的人工智能驱动框架对这些电量进行预测。本文详细介绍了所使用的人工智能模型，包括其实验验证。结果表明，该系统能够提供可靠的电气参数预测。对分布式测量系统和所收集数据的评估提供了有价值的见解，有助于采取更明智的行动来优化能源管理和系统性能。本研究的一个关键创新点在于探索跨测量节点的模型泛化。数据的相关性分析支持了这种方法，突出了在数据缺失情况下进行准确预测的潜力。此外，系统的易于部署和实际应用被强调为可扩展性的关键因素，使其有可能应用于类似的基础设施。
