Short-term transmission system losses forecast based on supervised machine learning
- Resource Type
- Conference
- Authors
- Sudic, Ivan; Mesar, Matko; Franc, Bojan; Capuder, Tomislav; Ivankovic, Tomislav; Pavic, Krunoslav; Pavic, Ivica
- Source
- 2020 International Conference on Smart Systems and Technologies (SST) Smart Systems and Technologies (SST), 2020 International Conference on. :199-204 Oct, 2020
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Propagation losses
Forecasting
Electrical engineering
Wind forecasting
Meteorology
Production
Europe
active power losses
short-term forecast
supervised machine learning
transmission system operator
- Language
Although active power losses in transmission networks are not significant in percentage, especially compared to the distribution networks, they constitute a major expense for the system operators. Predicting these losses and procuring them in a most feasible way becomes of out-most importance. The paper discusses the importance of short-term active power losses forecasting of different scales and proposes a model based on supervised machine learning to tackle the issue. Support vector regression method with weather forecasts as input data is validated on Croatian Transmission System Operators (HOPS) data, showing significant improvements as compared to business-as-usual approach. The developed model is integrated into a software tool and deployed at HOPS.