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

arXiv: https://arxiv.org/abs/2503.00331 · HTML: https://mlsanity.com/paper/2503.00331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 90 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Application of Segment Anything Model for Civil Infrastructure Defect Assessment](https://mlsanity.com/paper/2304.12600/) (2023)
- [Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions](https://mlsanity.com/paper/2304.11405/) (2023)
- [Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images](https://mlsanity.com/paper/2306.12510/) (2023)
- [Fog Computing Approaches in Smart Cities: A State-of-the-Art Review](https://mlsanity.com/paper/2011.14732/) (2020)
- [Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care](https://mlsanity.com/paper/2406.06616/) (2024)
- [Comparative Analysis of Gradient-Based Optimization Techniques Using Multidimensional Surface 3D Visualizations and Initial Point Sensitivity](https://mlsanity.com/paper/2409.04470/) (2024)
- [Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming](https://mlsanity.com/paper/2408.09044/) (2024)
- [Leveraging Digital Twin and Machine Learning Techniques for Anomaly Detection in Power Electronics Dominated Grid](https://mlsanity.com/paper/2501.13474/) (2025)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy](https://mlsanity.com/paper/2501.17621/) (2025)
- [Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security](https://mlsanity.com/paper/2410.01016/) (2024)
- [Multi-Objective Optimization of Water Resource Allocation for Groundwater Recharge and Surface Runoff Management in Watershed Systems](https://mlsanity.com/paper/2502.15953/) (2025)

## Built on by (most cited first, 2 of 2)

- [Proof of AutoML: SDN based Secure Energy Trading with Blockchain in Disaster Case](https://mlsanity.com/paper/2509.10291/) (2025)
- [Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management](https://mlsanity.com/paper/2606.08314/) (2026)

## Abstract (arXiv)

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.