Publications by authors named "Majid Khayatnezhad"
Article Synopsis
- Short-term electricity price prediction is essential for market participants to improve bidding strategies and reduce risks, influenced by factors like supply, demand, and weather conditions.
- The study introduces a new method combining Artificial Neural Networks (ANN) with a Snake Optimization Algorithm (SOA) to forecast prices in the Nord Pool market by optimizing the network structure and selecting relevant data.
- Experimental results show that this approach outperforms existing methods, achieving significantly lower error rates in price predictions for the DK-1 and SE-1 regions, making it a useful tool for better decision-making in the electricity market.
Microgrids are a promising solution for decentralized energy generation and distribution, offering reliability, efficiency, and resilience. These small-scale power systems can operate independently or connect to the main grid, providing greater reliability and resilience. However, integrating renewable energy into microgrids presents challenges due to their unpredictable nature and fluctuating load of electricity.
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