用物理模型+数字孪生+区块链优化智能建筑能耗，省电35%还更安全。
PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security
- 融合物理规律的神经网络与数字孪生实时调控能源使用。
- 预测误差低至MAE 0.237kWh，能源成本降低35%，可再生能源利用率达40%。
- 适合关注智慧能源、系统安全与低碳管理的研究者和从业者。
智能电网的发展亟需先进计算方法以提升能源预测与优化能力。本文提出一种融合深度强化学习（DRL）、物理信息神经网络（PINNs）与区块链（BC）的混合框架：利用数字孪生（DT）生成的数据训练DRL代理，实现能源消耗的实时优化；通过PINNs嵌入物理定律，确保模型准确性与可解释性；借助区块链保障智能电网内通信的安全透明。模型基于包含智能电表数据、可再生能源输出、动态电价及物联网设备采集用户偏好的多源数据集进行训练与验证。结果表明，该框架预测性能优异，均方误差（MAE）为0.237 kWh，均方根误差（RMSE）为0.298 kWh，决定系数（R²）达0.978，解释了97.8%的数据方差。分类指标显示准确率97.7%、精确率97.8%、召回率97.6%、F1分数97.7%。相比线性回归、随机森林、支持向量机、LSTM和XGBoost等传统模型，本方法在精度与实时适应性上表现更优。除提升能效外，系统使能源成本下降35%，用户舒适度维持在96%，可再生能源利用率提高至40%。研究展示了将PINNs、DT与区块链结合在智能电网中优化能源消费的巨大潜力，为构建可持续、安全、高效的能源管理系统提供了新路径。
原文摘要 · Abstract (English)
The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from Digital Twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) Blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R2) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model's robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and Blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。