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.

翻译：智能家居能源管理系统有助于配电网更高效、可靠地运行，并促进分布式可再生能源的有效渗透。此类系统依赖于稳健的预测、优化及控制/调度算法，以应对需求和可再生能源发电的不确定性。本文提出了一种先进的机器学习算法，即基于循环趋势预测神经网络的预测嵌入式调度方法（rTPNN-FES），用于实现高效的住宅需求控制。rTPNN-FES是一种新型神经网络架构，可同时预测可再生能源发电量并调度家用电器。凭借其嵌入式结构，rTPNN-FES消除了对独立预测和调度算法的需求，并生成对预测误差具有鲁棒性的调度方案。本文还评估了所提算法在物联网智能家居中的性能。评估结果表明，rTPNN-FES能够在比优化方法快37.5倍的速度下提供接近最优的调度方案，同时优于当前最先进的预测技术。