Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network. (15th June 2023)

• Record Type: Journal Article
• Authors: Nakıp, Mert; Çopur, Onur; Biyik, Emrah; Güzeliş, Cüneyt
• Is Part Of: Applied energy. Volume 340(2023)
• Publication Date: 2023-06-15
• Subjects: Energy management -- Forecasting -- Scheduling -- Neural networks -- Recurrent trend predictive neural network; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.121014
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: 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. Highlights: A novel rTPNN-based Forecast Embedded Scheduling (rTPNN-FES) is presented. rTPNN-FES simultaneously forecasts and schedules household loads. Significant improvement is achieved in forecasting compared to the state-of-the-art. Near optimal scheduling is achieved with much lower computation time.