Steel Industry – Energy Consumption Dataset

This dataset contains 15-minute interval energy usage records from a steel manufacturing facility. It includes continuous electrical measurements, CO₂ emissions, time-based features, and categorical indicators related to operational load and weekday/weekend status. The dataset is suitable for energy forecasting, industrial analytics, and smart factory research. Features Included Usage_kWh – energy consumption Reactive Power (Lagging/Leading) CO₂ emissions Power Factor (Lagging/Leading) NSM (Number of Seconds from Midnight) WeekStatus – Weekday or Weekend Day_of_week Load_Type – Light, Medium, or Maximum load Timestamp (date) Total instances: ~35040 records covering the full year 2018. Intended Use Ideal for machine learning tasks such as: Energy consumption prediction Load forecasting Peak demand analysis Industrial process optimization Machine learning modeling Source / Reference Sathishkumar V. E., Shin C., Cho Y.,Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city,Building Research & Information, 2021. Sathishkumar V. E. et al.,An Energy Consumption Prediction Model for Smart Factory using Data Mining Algorithms,KIPS Transactions on Software and Data Engineering, Vol. 9, No. 5, 2020. Sathishkumar V. E. et al.,Industry Energy Consumption Prediction Using Data Mining Techniques,International Journal of Energy Information and Communications, Vol. 11, 2020.