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

Nakıp, Mert; Çopur, Onur; Biyik, Emrah; Güzeliş, Cüneyt

doi:10.1016/j.apenergy.2023.121014

Cited by 15 publications

(1 citation statement)

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“…The results of a real-world case study show the superiority of MILP-based MPC over MILP and over experimental results. The authors of Nakıp et al [24] propose 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.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network-based home energy management system for smart homes

Ijala,

Idowu-Bismark,

Olugbeji

et al. 2024

Comput. Telecommun. Eng.

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<p>Energy efficiency is widely recognized as one of the most significant and economical ways to lower greenhouse gas (GHG) emissions. The aims and goals are that smart meters can evaluate and communicate in-depth real-time electricity usage, enable remote real-time monitoring and management of power consumption, and provide consumers with real-time pricing and analyzed usage information. The house energy management controller decides which loads will be powered based on the real home energy demands and the predefined load priorities. Artificial intelligence (AI) is being used increasingly in control applications due to its great effectiveness and efficiency. As a result, in this work, the author designed, simulated, and optimized an artificial neural network-based model simulation framework that simulates a home with a variety of home appliances and optimizes the total energy consumption of the home realistically through intelligent control of home appliances. The MATLAB application was used to model and examine the performance of four common household appliances: the water heater (WH), washing machine (WM), air conditioner (AC), and refrigerator (RG). The result shows a considerable reduction and savings in energy consumption without a decrease in consumer comfort.<strong></strong></p>

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Section: Introductionmentioning

confidence: 99%