01330nas a2200145   4500000000100000000000100001008004100002260002400043100001500067700001400082700002300096700002200119245012500141520091800266       2021                            d  bIEEEaBursa, Turkey1 aMert Nakip1 aArda Asut1 aCennet Kocabıyık1 aCüneyt Güzeliş00aA Smart Home Demand Response System based on Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic3 a<p>Distributing the peak load and alleviating grid stress by considering hourly electricity prices are some of the main research problems for current smart grid systems. This paper deals with the scheduling problem of home appliances' operating hours in smart grids, which aims to achieve minimum cost in user-defined operation intervals. To this end, scheduling via Artificial Neural Networks Augmented with Constraint Satisfaction Heuristic (ANN-AH) method that emulates the operation of the optimization for smart home demand response is developed. Our results show that a home demand response via ANN-AH achieves close to optimal performance with 10 times lower execution time than the optimal scheduling. These results suggest that the ANN-AH based demand response is highly successful and practical, and it is promising for future applications in micro-grid and decentralized renewable energy systems.</p>