REFERENCES

1. Pedrycz W. Welcome to the exciting world of “Green Computing and Smart Environments”. J Smart Environ Green Comput 2021;1:1-2.

2. ‘Aini NN, Subriadi AP. Governance and practice approach of green information technology. Procedia Computer Science 2022;197:650-9.

3. Patel YS, Mehrotra N, Soner S. Green cloud computing: a review on green IT areas for cloud computing environment. Available from: https://scirp.org/reference/referencespapers.aspx?referenceid=2547633 [Last accessed on 25 Apr 2022].

4. Radu L. Green cloud computing: a literature survey. Symmetry 2017;9:295.

5. Byun J, Hong I, Kang B, Park S. A smart energy distribution and management system for renewable energy distribution and context-aware services based on user patterns and load forecasting. IEEE Trans Consumer Electron 2011;57:436-44.

6. Lamnatou C, Chemisana D, Cristofari C. Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment. Renewable Energy 2022;185:1376-91.

7. Soomro AM, Bharathy G, Biloria N, Prasad M. A review on motivational nudges for enhancing building energy conservation behavior. J Smart Environ Green Comput 2021;1:3-20.

8. Soomro A, Paryani S, Rehman J, Echeverría R, Prasad M, Biloria N. Influencing human behaviour to optimise energy in commercial buildings. Available from: https://www.semanticscholar.org/paper/Influencing-Human-Behaviour-to-Optimise-Energy-in-Soomro-Paryani/3843f438956fffa349c5d94d35290b39169ddd68 [Last accessed on 25 Apr 2022].

9. Alarifi A, Dubey K, Amoon M, et al. Energy-efficient hybrid framework for green cloud computing. IEEE Access 2020;8:115356-69.

10. Collotta M, Pau G. An innovative approach for forecasting of energy requirements to improve a smart home management system based on BLE. IEEE Trans on Green Commun Netw 2017;1:112-20.

11. Liu Z, Zhang C, Dong M, Gu B, Ji Y, Tanaka Y. Markov-decision-process-assisted consumer scheduling in a networked smart grid. IEEE Access 2017;5:2448-58.

12. Marszałek A, Burczynski T. Forecasting day-ahead spot electricity prices using deep neural networks with attention mechanism. J Smart Environ Green Comput 2021;1:21-31.

13. Basmadjian R. Flexibility-based energy and demand management in data centers: a case study for cloud computing. Energies 2019;12:3301.

14. Kiani A, Ansari N. Toward low-cost workload distribution for integrated green data centers. IEEE Commun Lett 2015;19:26-9.

15. Chou L, Chen H, Tseng F, Chao H, Chang Y. DPRA: dynamic power-saving resource allocation for cloud data center using particle swarm optimization. IEEE Systems Journal 2018;12:1554-65.

16. Hasan MS, Kouki Y, Ledoux T, Pazat J. Exploiting renewable sources: when green SLA becomes a possible reality in cloud computing. IEEE Trans Cloud Comput 2017;5:249-62.

17. Cui X, Mills B, Znati T, Melhem R. Shadow Replication: an energy-aware, fault-tolerant computational model for green cloud computing. Energies 2014;7:5151-76.

18. Gai K, Qiu M, Zhao H, Tao L, Zong Z. Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. Journal of Network and Computer Applications 2016;59:46-54.

19. Fan Q, Ansari N, Sun X. Energy driven avatar migration in green cloudlet networks. IEEE Commun Lett 2017;21:1601-4.

20. Wu Y, Breaz E, Gao F, Miraoui A. A modified relevance vector machine for PEM fuel-cell stack aging prediction. IEEE Trans on Ind Applicat 2016;52:2573-81.

21. Coën A, Desfleurs A. The relative performance of green REITs: evidence from financial analysts’ forecasts and abnormal returns. Finance Research Letters 2022;45:102163.

22. Peng J, Kimmig A, Niu Z, Wang J, Liu X, Ovtcharova J. A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework. Applied Energy 2021;299:117321.

23. Farahnakian F, Ashraf A, Pahikkala T, et al. Using ant colony system to consolidate VMS for green cloud computing. IEEE Trans Serv Comput 2015;8:187-98.

24. Kashyap P, Kumar S, Dohare U, Kumar V, Kharel R. Green computing in sensors-enabled internet of things: neuro fuzzy logic-based load balancing. Electronics 2019;8:384.

25. Vale Z, Gomes L, Faria P, Ramos C. Artificial Intelligence to solve pervasive internet of things issues. Available from: https://www.elsevier.com/books/artificial-intelligence-to-solve-pervasive-internet-of-things-issues/kaur/978-0-12-818576-6 [Last accessed on 25 Apr 2022].

26. Faria P, Vale Z. Distributed energy resource scheduling with focus on demand response complex contracts. Journal of Modern Power Systems and Clean Energy 2021;9:1172-82.

27. Gazafroudi A, Soares J, Fotouhi Ghazvini MA, Pinto T, Vale Z, Corchado JM. Stochastic interval-based optimal offering model for residential energy management systems by household owners. International Journal of Electrical Power & Energy Systems 2019;105:201-19.

28. Ramos D, Teixeira B, Faria P, Gomes L, Abrishambaf O, Vale Z. Using diverse sensors in load forecasting in an office building to support energy management. Energy Reports 2020;6:182-7.

29. Ramos D, Khorram M, Faria P, Vale Z. Load forecasting in an office building with different data structure and learning parameters. Forecasting 2021;3:242-54.

30. Vale Z, Faria P, Abrishambaf O, Gomes L, Pinto T. MARTINE - a platform for real-time energy management in smart grids. Energies 2021;14:1820.

Journal of Smart Environments and Green Computing
ISSN 2767-6595 (Online)
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