BQEB-Data: An Open Benchmark Dataset for Autonomous Energy Intelligence and Smart Grid Analytics

The global transition toward decentralized, renewable-rich, and digitally managed power systems has increased the need for open benchmark datasets that support forecasting, optimization, and cyber-resilience research. Existing public datasets often focus on isolated aspects of energy systems such as household demand, weather, or electricity prices, limiting their usefulness for integrated smart-grid intelligence studies. This paper introduces BQEB-Data v1, a synthetic multimodal benchmark dataset inspired by the BIO-Quantum Energy Brain (BQEB) framework for next-generation autonomous energy systems. BQEB-Data v1 contains 10,512 chronologically ordered records sampled at 15-minute intervals and includes 37 variables spanning electricity demand, renewable generation, battery storage operation, electric vehicle charging activity, weather conditions, market pricing, grid quality indicators, and cyber anomaly events. The dataset is designed to support a wide range of machine learning and optimization tasks including short-term load forecasting, renewable output prediction, price estimation, battery dispatch scheduling, anomaly detection, and resilience analytics. Chronological train, validation, and test splits are provided to enable reproducible experimentation. Baseline benchmark experiments are presented using conventional machine learning models for forecasting and classification tasks. Results demonstrate that the dataset exhibits realistic temporal patterns and meaningful feature interactions suitable for algorithm development and comparative evaluation. BQEB-Data v1 provides researchers, industry practitioners, and policymakers with an extensible open resource for advancing intelligent grid management, sustainable infrastructure, and autonomous energy decision systems.