"Adaptive Smart Building Energy Management System Using Artificial Intelligence-Based Temporal Prediction"

"This dataset presents real-time electrical energy consumption data collected using an IoT-based Energy Management System (EMS) designed for smart building applications. The system utilizes embedded hardware including ESP8266 and PZEM-004T sensors to measure key electrical parameters such as voltage, current, power, and energy consumption. The collected data is transmitted to a cloud platform for storage and further analysis. The dataset is structured as time-series data and is intended for applications in energy forecasting, load prediction, and intelligent energy optimization.A key contribution of this work is the integration of a Transformer-based deep learning model for accurate energy consumption prediction, addressing limitations of traditional models in handling long-term dependencies. The dataset can be used for developing and benchmarking AI models for smart energy management, demand response, and building automation systems. This work supports research in sustainable energy utilization, real-time monitoring, and intelligent control strategies for reducing energy consumption and improving system efficiency."