This dataset is the first release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data. Competition Outline Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques. Releases Details v1.0: one year of data from a smart building with readings taken every 5 minutes. v2.0: 40 days of data from a smart building with readings taken every 5 minutes. v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025). v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period. Dataset Description All releases are composed of the following data: Time: in hours and minutes Power: in Watts Voltage: in Volts Current: in Ampers Generation power: in Watts Temperature: in ºC

The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.