In large low-power networks of battery-driven sen- sors, power outages are a major concern and communication rates have to be carefully designed in order to optimize energy consumption, network connectivity and sensors lifetime. In some IoT use cases, power can be supplied to sensors by way of renewable energy automatic harvesting (solar panels, etc.). Given the high variability of energy arrival processes, energy consump- tion in sensors, in particular caused by sensor transmissions to the sink, has to be then aligned with energy harvesting patterns, in order to maximize throughput while avoiding power outages that may arise when the battery is empty. This paper proposes ODMAC++, an extension to a well-known protocol for sensor transmission scheduling in a WSN. ODMAC++ relies on learning techniques to adapt sensors communication rate to energy harvesting patterns, and uses a beaconing mechanism for which the frequency is adjusted based on past measurements related to the harvested energy process. Simulations based on analytical energy arrival models and on real solar radiation measurements indicate that ODMAC++ is able to avoid power outages and to cope with battery limitation and energy variations due to variability in time.