  Smart home energy management systems help the distribution grid operate more
efficiently and reliably, and enable effective penetration of distributed
renewable energy sources. These systems rely on robust forecasting,
optimization, and control/scheduling algorithms that can handle the uncertain
nature of demand and renewable generation. This paper proposes an advanced ML
algorithm, called Recurrent Trend Predictive Neural Network based Forecast
Embedded Scheduling (rTPNN-FES), to provide efficient residential demand
control. rTPNN-FES is a novel neural network architecture that simultaneously
forecasts renewable energy generation and schedules household appliances. By
its embedded structure, rTPNN-FES eliminates the utilization of separate
algorithms for forecasting and scheduling and generates a schedule that is
robust against forecasting errors. This paper also evaluates the performance of
the proposed algorithm for an IoT-enabled smart home. The evaluation results
reveal that rTPNN-FES provides near-optimal scheduling $37.5$ times faster than
the optimization while outperforming state-of-the-art forecasting techniques.
