Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

TytułRenewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network
Publication TypeJournal Article
Rok publikacji2023
AutorzyNakip M, Çopur O, Biyik E, Güzeliş C
JournalApplied Energy
Volume340
Start Page121014
Date Published03/2023
ISSN0306-2619
Słowa kluczoweEnergy management, Forecasting, Neural networks, Recurrent trend predictive neural network, scheduling
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.

URLhttps://authors.elsevier.com/a/1gr4J15eif8Fs5
DOI10.1016/j.apenergy.2023.121014

Plik PDF: 

Historia zmian

Data aktualizacji: 07/07/2023 - 09:58; autor zmian: Mert Nakip (mnakip@iitis.pl)