Electrical consumption and demand Dataset from the Federal Institute of Espírito Santo, Serra, Brazil
Description
The "Electrical consumption and demand Dataset from the Federal Institute of Espírito Santo, Serra, Brazil" provides real-world telemetry measurements of electricity consumption and demand from a public campus integrated with a 119 kWp photovoltaic system. Spanning 45 months from October 2022 to July 2026, the dataset comprises over 116,000 records sampled at 15-minute intervals. Measurements were collected using a bidirectional ISSO three-phase power analyzer (DMI F3000R v2 True RMS) installed at the main low-voltage switchboard (QGBT) of the IFES Serra campus. Recorded parameters include voltage, current, frequency, apparent, reactive, and active (direct/reverse) power, power factors, and phase unbalance metrics. The dataset is hierarchically structured into raw and processed folders. The raw data directory (raw_data) contains unedited CSV files (with a .xls extension) featuring original Portuguese headers organized in yearly subdirectories. The processed directory (processed_data) offers consolidated Excel files (.xlsx) with English-translated headers, timestamps synchronized to local time (UTC-3) and UTC, active power scaled to kW, and cumulative active energy computed in kWh for direct consumption and reverse PV injection. An automated four-step quality validation protocol assessed the data: chronological consistency checks identified 80 time-series gaps from power or telemetry outages; ANEEL PRODIST signal quality filtering flagged 346 voltage readings outside the 117–133 V threshold; power-balance checks confirmed phase-summation consistency within 0.1 W/VA; and Ohm's law cross-validation detected only two entries with relative error exceeding 1%. Limitations include aggregated switchboard-level metering without individual load disaggregation, a 15-minute interval that misses fast transients, lack of meteorological data, and load patterns specific to an educational institution. Overall, the dataset provides authentic empirical measurements capturing real-world operational and seasonal variability. It serves as a benchmark for machine learning models in short- and long-term demand forecasting, microgrid management, economic dispatch, and techno-economic sizing of battery energy storage systems (BESS).
Files
Steps to reproduce
To reproduce and work with the processed dataset (processed_data), users can directly utilize the consolidated Excel files organized into annual subdirectories following the naming convention YYYY_MM_Processed_ISSO.xlsx. As raw data extraction requires private login credentials for the web portal (Datalog ISSO), the automated processing pipeline transforms raw measurements into the standardized processed format through the following sequence: (1) reading raw export files, stripping initial metadata header lines, and standardizing/translating all column headers from Portuguese to English using the mapped parameter correlation (e.g., converting "Inicio" and "Fim" to "UTC-3 Start" and "UTC-3 End", "Frequencia" to "Frequency", etc.); (2) normalizing sampling timestamps and creating UTC-synchronized time columns ("UTC Start" and "UTC End") repositioned at the table's start alongside local time (UTC-3); (3) converting direct and reverse active power parameters from Watts (W) to kilowatts (kW); (4) calculating cumulative active energy in kilowatt-hours (kWh) for both direct flow (energy consumed from the utility grid) and reverse flow (surplus photovoltaic energy exported to the grid) based on the 15-minute sampling interval; and (5) running a four-step automated validation protocol that evaluates chronological continuity (flagging time gaps where the time interval are greater than 15 min), filters operational voltage violations outside ANEEL PRODIST Module 8 thresholds (117–133 V), verifies total three-phase active and apparent power balance consistency, and performs Ohm's law cross-checks (flagging relative error > 1.0%), saving all output logs in the validation_logs folder. Researchers can load these cleaned and validated .xlsx files directly into data analysis environments (such as Python/Pandas or R) to reproduce machine learning model training, short- and long-term demand forecasting, microgrid management simulations, and BESS sizing studies.
Institutions
- Instituto Federal de Educacao Ciencia e Tecnologia do Espirito SantoES, Vitoria
Categories
Funders
- Fundação de Amparo à Pesquisa do Espírito SantoEspírito Santo, BrazilGrant ID: Nº 1228/2022 - Nº SIAFEM:2022-CD0RQ, TECNOVA II 002/2021 and 850/2023 - Nº SIAFEM:2023-DLP0J