Part 8/14:

• Maintaining the human-in-the-loop for critical decisions.

• Implementing continuous feedback loops for learning and improvement.

A noteworthy recommendation was deploying AI in a phased manner—starting with pilot projects, then scaling up as systems prove their value, all while managing risks judiciously.

Practical Use Cases: Energy Optimization and Indoor Air Quality

Real-world applications demonstrated AI's potential:

• Energy Use and Demand Forecasting: Utilized RNN and LSTM models to analyze utility data and predict energy demands, factoring in external weather, occupancy, and historical patterns. This enabled proactive management of energy consumption, leading to substantial savings.