DOI: 10.5281/zenodo.18627488
title: GridLog-Cascade 2025

description: The Smart-Grid–Logistics Cyber-Physical Dataset (SGL-CPD 2025) is a real-world multivariate time-series dataset capturing the operational coupling between smart grid infrastructure, renewable energy integration, battery storage systems, cold-chain warehouse logistics, EV fleet charging networks, and industrial cybersecurity telemetry. The dataset reflects practical deployment environments where distribution grid operators, cold-chain logistics providers, and industrial IoT systems operate in an interconnected cyber–physical ecosystem.

Data were collected from synchronized monitoring streams typically available in SCADA-based grid supervision systems, renewable generation controllers, warehouse management platforms, refrigeration control systems, EV charging stations, and cybersecurity monitoring logs. The dataset models realistic operational behavior under renewable variability, demand–supply imbalance, grid stress, automation load, and cyber intrusion events.

The dataset contains 476,716 time-stamped records sampled at 5-minute intervals, ending at 2025-12-25 23:55:00 (inclusive). Each record represents a synchronized operational snapshot of grid, logistics, and cybersecurity states. The data are suitable for multi-task modeling, including cyberattack detection (multi-label classification), grid load forecasting (multi-horizon regression), and cascading grid failure severity prediction (multi-class classification). The class distribution reflects real operational imbalance, where normal behavior dominates, cyberattacks are rare, and severe cascading failures are very rare events.



Feature Groups

1. Time and Calendar FeaturesTimestamp and temporal indicators used for time-series modeling:





timestamp




hour




dayofweek




month




dayofyear




year




is_weekend




hour_sin, hour_cos




doy_sin, doy_cos





2. Renewable Generation and Grid Operation FeaturesThese features describe renewable integration, grid demand, stability, and stress conditions:





solar_irradiance_wm2




pv_power_mw




wind_speed_ms




wind_power_mw




renewable_supply_mw




gen_fluctuation_index




total_load_mw




demand_supply_gap_mw




grid_frequency_hz




voltage_deviation_pct




transformer_load_pct




line_congestion_index





3. Energy Storage and Backup Support FeaturesBattery and backup generation characteristics representing grid resilience:





battery_soc_pct




battery_charge_kw




battery_discharge_kw




backup_gen_status




backup_autonomy_hr





4. Cold-Chain Warehouse and Environmental FeaturesThese features capture refrigeration stability, environmental control, and warehouse energy usage:





warehouse_temp_c




refrigeration_load_kw




humidity_pct




cold_storage_uptime_pct




warehouse_energy_kwh





5. Warehouse Automation and Throughput FeaturesOperational metrics from automated logistics systems:





robot_utilization_pct




conveyor_speed_mps




inventory_throughput_items_hr




order_processing_delay_min





6. EV Fleet Charging and Dispatch FeaturesElectric fleet operational and charging indicators:





charging_station_load_kw




fleet_soc_mean_pct




charging_wait_min




vehicle_dispatch_delay_min




route_completion_rate_pct





7. Cybersecurity Telemetry FeaturesSecurity monitoring indicators derived from industrial control and IoT systems:





scada_login_anom_cnt




cmd_injection_alerts_cnt




firmware_hash_mismatch




network_latency_ms




intrusion_severity_score





8. Cross-Domain Coupling FeaturesEngineered indicators quantifying interdependence between energy and logistics operations:





energy_to_cooling_ratio




charging_to_generation_ratio




logistics_energy_criticality_score




cascade_propagation_lag_min





Target Variables

Attack_Event (Multi-Label Classification)





attack_primary_class (0–7)




attack_bitmask




attack_fdi




attack_dos




attack_cmd_inj




attack_replay




attack_meas_tamper




attack_malware




attack_load_alt



Grid_Load_Forecast (Regression Targets)





y_load_tplus_1h_mw




y_load_tplus_6h_mw




y_load_tplus_24h_mw



Cascading_Grid_Failure (Multi-Class Severity Label)





cascade_failure_severity (0–3)