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An accurate weather forecast is the basis for the valuation of weather derivatives, securities that partially compensate for financial losses to holders in case of, from their perspective, adverse outside temperature. The paper analyses precision of two forecast models of average daily temperature, the Ornstein-Uhlenbeck process (O-U process) and the generalized autoregressive conditional heteroskedastic model (GARCH model) and presumes for the GARCH model to be the more accurate one. Temperature data for the period 2000-2017 were taken from the DHMZ database for the Maksimir station and used as the basis for the 2018 forecast. Forecasted values were compared to the available actual data for 2018 using MAPE and RMSE methods. The GARCH model provides more accurate forecasts than the O-U process by both methods. RMSE stands at 3.75 °C versus 4.53 °C for the O-U process and MAPE is 140.66 % versus 144.55 %. Artificial intelligence and supercomputers can be used for possible improvements in forecasting accuracy to allow for additional data to be included in the forecasting process, such as up-to-date temperatures and more complex calculations.

FLEXITRANTORE as a HORIZON 2020 project aims to develop an integrated platform for the next generation flexible electricity transmission system. During the project the investigation and implementation of state-of-the-art technologies will be realized, like storage systems, new market designs and business models, Dynamic Line Rating (DLR) technology or de-icing methodologies. The objective of FLEXITRANTORE’s WP7 work package is to develop a DLR based anti-icing technology for transmission lines, therefore make the grid more reliable [1].

The electric utility industry is restructuring itself to operate in a competitive wholesale market. However, the transmission system remains a regulated entity that connects deregulated generation with the end consumer. In many countries, the pace of investment in OHLs has lagged behind the rate of load growth and generated additional capacities, due to public, regulatory, environmental and financial obstacles to the construction of new transmission facilities. Consequently, many OHLs reached critical values of ampacity and sag. Many renewable energy sources, especially hydro plants, solar or wind farms also require dynamic operation of the power grid. OTLM – Overhead Transmission Line Monitoring system is adding new dimensions to the operation of OHLs enables more efficient performance while at the same time enhances the safety of system operation. A maximum utilization of the OHL ampacity is only possible, if the operators have accurate data about the actual ground clearance, crossed lines, vegetation, instantaneous conductor temperature and current. With the measurements captured and processed by OTLM, the operator of the transmission network can optimize and determine the operation mode of OHLs. Software solutions also provide the means for a short-term prediction of conductor temperature. Temperature data along with data gained by laser scanning or similar measurements and diagnostics of the OHLs is necessary to up-rate OHL projects. This paper is supported with case studies, which prove that temperature and sag monitoring is an essential part of the transmission smart-grid.

OTLM (Overhead Transmission Line Monitoring) is a system solution for monitoring and rating of existing and new overhead lines (OHL) based on real-time monitoring of conductor temperature, sag, load, and weather conditions in order to ensure save maximum utilization of transmission line ampacity. Successful development and more than 10 years' of experience in using the OTLM system on transmission networks in several countries all over the world has initiated some system upgrades to ensure even more crucial information on OHL behaviour. The existing functionality of conductor temperature measurements (used for static rating-STR) was initially combined with a weather station measurement that enables the determination of dynamic rating (DTR). With additional inclination (sag/tension) measurements, we developed the OTLM-SAG application. With this additional feature we are able to determine the sag on critical spans. This additional information is especially useful in case of OHL crossings over roads, railroads, other overhead lines, etc. Additionally, measured data like angle (sag) is implemented to the software application to detect ice overloads or fallen trees. The mathematical relationship between the conductor's tensile force and sag is crucial for the calculation of the conductor's expansion (tension) and final length over a constant span distance. The reliability of ice thickness calculations mainly depends on the accuracy of conductor temperature and angle/sag measurements. The OTLM-ICE application enables the operator of the transmission network to monitor sag and clearance changes on a conductor, subjected to ice overloads. The operator can optimize and determine the suitable ampacity of transmission lines in order to prevent the damage in early phases of ice-rain.