As a key component of modern energy management systems, energy demand forecasting plays a crucial role in efficient resource allocation, grid stability, and the integration of renewables. Traditionally, classical techniques such as ARIMA and SARIMA have been used for energy demand prediction due to their simplicity and interpretability. However, these conventional methods often fail to capture dynamic interactions and nonlinear dependencies present in contemporary energy systems.
With recent advancements in Artificial Intelligence (AI), particularly in Machine Learning (ML) and Deep Learning (DL), energy demand forecasting has undergone a significant transformation. AI models that leverage real-time data, socioeconomic factors, and external variables have demonstrated superior performance compared to traditional approaches, not only in predicting energy demand but also in optimizing energy distribution, managing renewables, and reducing operational costs.
Furthermore, the combination of AI and advanced technologies, such as smart grids and the Internet of Things (IoT), has further enhanced energy efficiency and system reliability across various industries. These technologies enable real-time monitoring and control of energy consumption, leading to better decision-making and more sustainable energy usage.
This project aims to apply state-of-the-art AI techniques to analyze data from Croatia’s Energy Management Information System, with the objective of improving its efficiency and adaptability. The research will take into account the specific characteristics of the Croatian energy system, addressing critical challenges such as data quality, measurement gaps, computational complexity, and privacy concerns.
By tackling these challenges, the project seeks to unlock the full potential of AI in enhancing the efficiency and sustainability of Croatia’s energy infrastructure, ultimately contributing to a more resilient and intelligent energy management system.
