Hey, totally! I’d start with a tiny micro‑service in Python, hook it up to every smart meter in the city via MQTT, then feed the stream into a lightweight reinforcement‑learning model. The agent would observe the current load, predict short‑term demand, and shift non‑critical appliances like street lights or water pumps by sending commands over the same MQTT bus. Add a simple dashboard with Grafana so the city planners can tweak the reward function—like give a bonus for hitting peak‑off‑peak targets. Once the model converges, it can run on a single edge device per district, keeping latency low and the power budget tight. The best part? You can swap out the RL algorithm for a rule‑based tweak and see the difference in real time—talk about satisfying debugging!