The idea concerning the control strategy of a Solid Oxide Fuel Cell (SOFC) functioning to meet the electrical demand of a public utility building is meticulously detailed. This innovative strategy was thoughtfully designed and structured with the integral assistance of an Artificial Neural Network, a type of artificial intelligence that models human brain function and can adapt to new data.

This complex network, the Artificial Neural Network, was employed for a critical function; it was used to forecast the electricity demand, a task requiring significant computational intelligence and adaptability. These intricate calculations and simulations were performed specifically using the example of a prominent structure, the building of the Institute of Heat Engineering at Warsaw University of Technology.

The control strategy's effectiveness and operation aren't static, they are significantly influenced by a multitude of diverse factors. These factors could be internal or external, varying with the dynamic changes in market conditions, as well as the operating characteristics of the SOFC itself. As a result, we can effectively define several different objective functions tailored to meet the circumstances. These objectives can range from operating solely for self-sustenance, to functioning for maximum profitability, and even to achieving the longest possible service life.

Moreover, the article goes on to showcase a comprehensive simulation of the SOFC's operation, specifically tailored to the electricity demand profile of the aforementioned Institute of Heat Engineering (IHE) building. The simulation takes into account the data from a selected period of time, providing a rich and detailed view of the SOFC's potential capabilities and performances under various operating conditions and demand scenarios. This case study acts as a demonstration of the practical application of the control strategy and offers potential insights for its broader implementation.