Replication Data for: BIO-Quantum Energy Brain (BQEB)

Master Theme BIO-Quantum Energy Brain (BQEB) A unified intelligent framework for next-generation energy systems integrating AI, bio-inspired adaptation, quantum optimization, and secure autonomous control. Recommended 5-Paper Publication Roadmap: 1. Framework Paper (Foundational Signature Paper) Title: BIO-Quantum Energy Brain: A Unified Intelligence Framework for Smart Grids, Storage, and Renewable Energy Systems ==> Completed Purpose Define the field and architecture. Core Contents 7-layer system design AI + quantum + bio-inspired modules control loops governance model use cases 2. Open Dataset Paper Title: BQEB-Data: An Open Multimodal Benchmark Dataset for Intelligent Energy Forecasting and Grid Optimization == Completed Purpose Create reusable data asset. Include Data Types smart meter demand solar generation wind generation battery state-of-charge EV charging demand weather pricing outage events 3. Forecasting Benchmark Paper Title: Benchmarking AI Models on BQEB-Data for Multi-Horizon Energy Forecasting == Completed Compare LSTM Transformer XGBoost Temporal Fusion Transformer Graph Neural Nets Metrics MAE RMSE MAPE latency Use Cases load demand solar output charging demand 4. Optimization Paper Title: Quantum and Bio-Inspired Optimization for Energy Dispatch in the BQEB Framework Compare linear programming genetic algorithms particle swarm simulated annealing quantum-inspired solvers Use Cases battery dispatch EV charging schedule renewable curtailment minimization grid balancing 5. Security / Resilience Paper Title: Secure and Resilient BQEB: Cyber-Physical Defense for Autonomous Energy Networks Focus false data injection detection cyberattack resilience anomaly detection self-healing recovery adversarial robustness BQEB-Data v1(BIO-Quantum Energy Brain Dataset for Smart Grid, Storage, Renewable Energy, EV Charging, and Cyber-Resilient Energy Intelligence) is a synthetic multimodal benchmark dataset developed to support research in next-generation intelligent energy systems. The dataset is inspired by the BIO-Quantum Energy Brain (BQEB) concept, which integrates artificial intelligence, bio-inspired adaptive control, quantum optimization, and secure autonomous decision-making for modern energy infrastructure. BQEB-Data v1 contains 10,512 time-series records sampled at 15-minute intervals and includes variables representing electricity demand, renewable generation, battery storage behavior, electric vehicle charging activity, weather conditions, electricity market pricing, grid health indicators, and cyber anomaly events. The dataset is designed for benchmarking machine learning, forecasting, optimization, anomaly detection, reinforcement learning, and cyber-physical resilience methods. It enables use cases such as load prediction, renewable energy forecasting, dynamic pricing analysis, battery dispatch scheduling, EV charging orchestration, outage prediction, and security analytics. Chronological train, validation, and test splits are included to support reproducible experimentation. BQEB-Data v1 serves as an open research resource for academia, industry, and government researchers working on smart grids, sustainable energy systems, and autonomous infrastructure intelligence.