Residential applications of solar PV systems represent both an emerging trend and a promising solution for mitigating greenhouse gas emissions from the housing sector and reducing energy costs for homeowners. To achieve optimal energy performance and a high load match index, the electricity generated by the solar PV system should be used on-site as much as possible with minimal interaction with the utility grid. Therefore, novel technologies and tools for energy management of solar PV-integrated homes are urgently needed. Home Energy Management Systems (HEMS) have seen increasing popularity and adoption in recent years. However, existing HEMS lack a critical component for achieving a high load match index in homes integrated with solar PV and IoT (Internet of Things) appliances/devices, as these HEMS do not use energy generation forecasting to schedule household activities in a manner that optimizes use of the generated energy. In this context, in this research we will develop multi- temporal solar PV energy generation prediction models that employ machine learning and deep learning to accurately predict the daily, hourly, and minutely energy generation based on the location of the house, solar PV layout (i.e., orientation, tilt angle, etc.) and other system parameters (i.e., number of panels, capacity, efficiency, etc.) and weather prediction. Then, a framework will be developed to use the energy generation predictions for scheduling the household activities that consume a significant amount of energy (such as washing and drying, dishwashing, ironing, water heating, air conditioning, recharging the storage, and charging of electric vehicle) using IoT. The proposed framework will bridge a very critical gap in the energy management of solar PV-integrated energy-efficient homes (and/or NetZero Energy Homes), using IoT to help homeowners reduce energy costs and while mitigating the adverse effects of increased residential solar PV use on the utility grid (or smart grid).