Energy security is a crucial issue for political, environmental, and economic reasons. This article presents a modern approach to securing critical infrastructure in energy transmission networks, which are managed by advanced IT systems. This paper focuses on the integration of cryptographic mechanisms with biometric data, providing an additional layer of protection against cyber threats. The discussed solutions enable the protection of management systems in energy transmission networks, enhancing their resilience to cyberattacks. The use of the command-line interface (CLI) in combination with biometrics allows for precise execution of security tasks such as network monitoring, firewall management, and automation of security tasks. This makes these systems more reliable and secure, which is essential for the stability of energy systems.

One of the significant contributors to global warming is electricity production based on conventional fossil fuels, which exert a considerable environmental burden. This dependency can be mitigated through the increased utilization of Renewable Energy Resources (RERs), which offer both environmental and economic advantages. However, one of the main challenges associated with RERs, particularly wind energy, is their frequent installation at locations remote from load centers, which poses difficulties in efficient energy transmission. In this study, wind speed and power have been forecasted using three types of artificial neural networks: Feedforward Neural Network (FFNN), Cascaded Forward Neural Network (CFNN), and improved Feedforward Neural Network (id-FFN). The dataset used in this work was obtained from the Global Wind Atlas and covers the Silesian Region of Poland for the year 2023. Data from January to June were used to train the neural networks using a modified version of the Metaheuristic Hybrid Particle Swarm Optimization with Bat Algorithm Acceleration Coefficients (MHPSO-BAAC-x). The trained models were then employed to predict wind speed and power for the period from July to December 2023. To assess the accuracy of the forecasts, the following statistical error metrics were applied: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). The predicted values were subsequently compared with the actual data for the same period to evaluate the effectiveness of the models. The id-FFN model demonstrated the best performance, achieving values of 0.0262 m/s for MAE, 2.62% for MAPE, and 0.0152 m/s for RMSE, confirming its high precision and reliability. The obtained results suggest that the developed forecasting system has the potential to support future planning and integration of wind power stations into the national power system.