01672nas a2200181   4500000000100000000000100001008004100002260001200043100001500055700001600070700001600086700002200102245014200124856005000266490000800316520115200324022001401476       2023                            d  c03/20231 aMert Nakip1 aOnur Çopur1 aEmrah Biyik1 aCüneyt Güzeliş00aRenewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network  uhttps://authors.elsevier.com/a/1gr4J15eif8Fs50 v3403 a<p>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.</p>
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