具有增量学习的自适应物联网架构,用于在线太阳能生产预测

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Giuseppe Del Fiore, Teodoro Montanaro, Ilaria Sergi, Luigi Patrono
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引用次数: 0

摘要

智能家居通过整合光伏系统等可再生能源,在推进能源可持续性方面发挥着关键作用。它们的有效性取决于能源生产和消费之间更紧密的联系。然而,由于气象和季节因素的变化,预测太阳能仍然具有挑战性。传统的预测方法主要依赖于复杂的模型和静态数据集,缺乏基于动态输入的在线估计,如实时天气和来自物联网(IoT)设备的实际生产数据。虽然基于物联网的数据采集已经开始增强预测能力,但这些设备的异构性带来了互操作性挑战,限制了它们的全部潜力。此外,现有的模型往往不能利用增量学习,这对于随着新数据的出现而不断调整预测是必不可少的。为了缓解这些限制,本文提出了一种用于太阳能预测的模块化、可互操作和可扩展的物联网架构。它集成了多个模块:(a)集成异构物联网设备和外部服务,如天气预报,以获取实时数据;(b)结合光伏系统领域知识的基线模型,在没有历史数据的情况下提供初步的产量估计;(c)利用增量混合预测技术,将基于历史数据的长期趋势预测的批处理模型与用于在线短期预测的渐进式精细综合基线结合起来。所提出的架构已经在现实世界的智能家居场景中实现和评估。结果表明,该方法能够在保持较低计算复杂度的同时,以90%以上的准确率预测光伏发电量,强调了其在智能家居环境中的实际适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive IoT architecture with incremental learning for on-line solar production forecasting
Smart homes play a pivotal role in advancing energy sustainability by incorporating renewable energy sources, like photovoltaic systems. Their effectiveness depends on the closer alignment between energy production and consumption. However, forecasting solar energy remains challenging due to variability from meteorological and seasonal factors. Traditional forecasting methods primarily rely on complex models and static datasets, lacking on-line estimation based on dynamic inputs like live weather and actual production data from Internet of Things (IoT) devices. While IoT-based data acquisition has begun to enhance forecasting, the heterogeneity of these devices poses interoperability challenges, limiting their full potential. Moreover, existing models often fail to leverage incremental learning, which is essential for continuously adapting predictions as new data becomes available. To mitigate these constraints, this paper proposes a modular, interoperable, and scalable IoT architecture for solar energy forecasting. It incorporates modules to: (a) integrate heterogeneous IoT devices and external services, such as weather forecasting, to obtain real-time data; (b) incorporate a baseline model, informed by domain knowledge of photovoltaic systems, to provide initial production estimations in the absence of historical data; and (c) exploit incremental hybrid forecasting techniques able to combine a batch model for long-term trend prediction based on historical data and with a progressive refined integrated baseline for on-line short-term forecasting. The proposed architecture has been implemented and evaluated in a real-world smart home scenario. Results demonstrate its ability to predict photovoltaic energy production with over 90% accuracy while maintaining low computational complexity, underscoring its practical applicability in smart home environments.
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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