IESR: Instant Energy Scheduling Recommendations for Cost Saving in Smart Homes

Creative Commons License

Fakhar M. Z., Yalçın E., Bilge A.

IEEE ACCESS, cilt.10, ss.52178-52195, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/access.2022.3174073
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.52178-52195
  • Anahtar Kelimeler: Home appliances, Costs, Smart homes, Energy consumption, Optimal scheduling, Real-time systems, Job shop scheduling, Energy cost saving recommendations, off-peak scheduling, peak demand optimization, energy consumption awareness, MICRO-MOMENTS, DEMAND, CONSUMPTION
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

The exponential increase in energy demands continuously causes high price energy tariffs for domestic and commercial consumers. To overcome this problem, researchers strive to discover effective ways to reduce peak-hour energy demand through off-peak scheduling yielding low price energy tariffs. Efficient off-peak scheduling requires precise appliance profiling to identify a scheduling recommendation for peak load management. We propose a novel off-peak scheduling technique that provides instant energy scheduling recommendations by monitoring appliances in real-time following user-devised criteria. Once an appliance operates during a peak hour and fulfills the user criteria, a real-time scheduling recommendation is presented for users' approval. The proposed technique utilizes appliance energy consumption data, user-devised criteria, and energy price signals to identify the recommendation points. The energy cost-saving performance of the proposed technique is evaluated using two publicly available real-world energy consumption datasets with four price signals. Simulation results show a significant cost-saving performance of up to 84% for the experimented datasets. Moreover, we formulate a novel evaluation metric to compare the performance of various off-peak scheduling techniques on similar criteria. Comparative analysis indicates that the proposed technique outperforms the existing methods.