Large Language Models (LLMs) are reshaping Internet of Things (IoT) application development. They let developers generate functional code from natural language. Yet battery-powered IoT devices operate under tight energy budgets. Current LLM-driven programming frameworks offer limited support for power-constrained IoT design. In this paper, we propose GPowerT, an LLM-driven automated programming system for power-constrained IoT applications. GPowerT extracts power-related constraints from natural language requirements, including battery capacity, target lifetime, and energy-saving policies, dependent on hardware power profiles and a low-power design knowledge base. During code generation, it injects strategies such as deep sleep, periodic sampling, and batch communication. A static power estimation module analyzes operation and sleep cycles, communication frequency, and peripheral usage. It predicts total energy consumption and checks compliance with the budget. If the target is not met, the system pinpoints overconsumption and returns optimization hints to the LLM, enabling closed-loop refinement. Evaluations on representative IoT tasks show that GPowerT improves power-budget compliance and developer efficiency. The generated programs achieve long-duration battery life performance, which demonstrates the system’s potential and practicality for low-power IoT application development.