PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security

2025-03-01

AI Summary

Addressing the challenges of balancing accuracy, interpretability, and secure multi-stakeholder data collaboration in smart building energy optimization, this paper proposes the PINN-DT-BC tripartite synergistic framework. It integrates physics-informed neural networks (PINNs) to embed thermodynamic constraints—enhancing model interpretability and generalization; digital twin (DT) technology for cyber-physical co-simulation and closed-loop control; and blockchain (BC) to ensure trustworthy, auditable, and decentralized sharing of heterogeneous IoT data across multiple stakeholders. The framework incorporates deep reinforcement learning (DRL) for real-time, adaptive decision-making. Experimental results demonstrate a prediction MAE of 0.237 kWh and R² of 0.978; overall energy efficiency improves significantly, with 35% reduction in energy costs, 40% renewable energy utilization rate, and 96% user thermal comfort maintenance. This work achieves, for the first time, the deep integration of physics-based modeling, real-time simulation, and decentralized trust mechanisms.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Optimize real-time energy consumption in smart buildings.
• Integrate physical laws for accurate energy optimization models.
• Ensure secure communication in smart grids using blockchain.

Innovation

• Deep Reinforcement Learning optimizes energy in real-time.
• Physics-Informed Neural Networks embed physical laws.
• Blockchain ensures secure smart grid communication.