Mert NakıpOnur ÇopurEmrah BiyikCüneyt GüzelişCopur, OnurGuzelis, CuneytNakip, MertBiyik, Emrah2025-10-06202318729118, 030626190306-26191872-911810.1016/j.apenergy.2023.1210142-s2.0-85151292003https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151292003&doi=10.1016%2Fj.apenergy.2023.121014&partnerID=40&md5=fae9c79cf95edde387e924c6cf5c1654https://gcris.yasar.edu.tr/handle/123456789/8441https://doi.org/10.1016/j.apenergy.2023.121014Smart 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. © 2023 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessEnergy Management, Forecasting, Neural Networks, Recurrent Trend Predictive Neural Network, Scheduling, Automation, Domestic Appliances, Energy Efficiency, Energy Management Systems, Forecasting, Intelligent Buildings, Network Architecture, Recurrent Neural Networks, Renewable Energy Resources, Distribution Grid, Home Environment, Network-based, Neural-networks, Predictive Neural Network, Recurrent Trend Predictive Neural Network, Renewable Energies, Scheduling, Smart Home Energy Management Systems, Smart Homes, Energy Management, Algorithm, Alternative Energy, Artificial Neural Network, Energy Efficiency, Energy Management, Error Analysis, Penetration, Performance Assessment, Smart GridAutomation, Domestic appliances, Energy efficiency, Energy management systems, Forecasting, Intelligent buildings, Network architecture, Recurrent neural networks, Renewable energy resources, Distribution grid, Home environment, Network-based, Neural-networks, Predictive neural network, Recurrent trend predictive neural network, Renewable energies, Scheduling, Smart home energy management systems, Smart homes, Energy management, algorithm, alternative energy, artificial neural network, energy efficiency, energy management, error analysis, penetration, performance assessment, smart gridSchedulingRecurrent Trend Predictive Neural NetworkEnergy ManagementForecastingNeural NetworksArticle