The field of Internet of Things (IoT) and energy management is rapidly evolving with a focus on improving efficiency, reducing power consumption, and enhancing decision-making capabilities. Recent developments have centered around the integration of artificial intelligence, machine learning, and deep learning techniques to optimize energy management systems, predict energy demand, and improve grid stability. The use of Low Power Wide Area Networks (LPWAN) technologies, such as LoRaWAN, has also gained significant attention for enabling long-range, low-power communication in IoT applications. Furthermore, researchers have been exploring the application of probabilistic forecasting methods to better quantify uncertainty in energy systems and improve risk management. Notable papers in this area include: LIMA, a protocol for augmenting LoRaWAN deployments with a mesh network, which significantly increases the effective coverage range and reduces energy consumption. The introduction of a holistic AI-driven IoT energy management framework, which provides a structured approach to reducing power consumption and improving grid stability. A deep learning framework for modeling and dispatching hybrid wind farm power generation, which demonstrates improved robustness and accuracy in predicting wind energy output.